database assignment example

Browse Course Material

Course info, instructors.

• Prof. Samuel Madden
• Prof. Robert Morris
• Prof. Michael Stonebraker
• Dr. Carlo Curino

Departments

• Electrical Engineering and Computer Science

As Taught In

• Information Technology
• Algorithms and Data Structures
• Data Mining
• Software Design and Engineering

Learning Resource Types

Database systems, assignments.

This section contains problem sets, labs, and a description of the final project. Some assignments require access to online development tools and environments that may not be freely available to OCW users. The assignments are included here as examples of the work MIT students were expected to complete.

Problem Sets

SimpleDB overview ( PDF )

SimpleDB documentation

Final Project Assignment and Ideas

A large portion (20%) of your grade in 6.830 consists of a final project. This project is meant to be a substantial independent research or engineering effort related to material we have studied in class. Your project may involve a comparison of systems we have read about, an application of database techniques to a system you are familiar with, or be a database-related project in your research area.

This document describes what is expected of a final project and proposes some possible project ideas.

What Is Expected

Good class projects can vary dramatically in complexity, scope, and topic. The only requirement is that they be related to something we have studied in this class and that they contain some element of research — e.g., that you do more than simply engineer a piece of software that someone else has described or architected. To help you determine if your idea is of reasonable scope, we will arrange to meet with each group several times throughout the semester.

What to Hand In

There are two written deliverables, a project proposal and a final report.

Project Proposal : The proposal should consist of 1-2 pages describing the problem you plan to solve, outlining how you plan to solve it, and describing what you will “deliver” for the final project. We will arrange short meetings with every group before the project proposal to help you refine your topic and would be happy to provide feedback on a draft of your proposal before it is due.

Final Report : You should prepare a conference-style report on your project with maximum length of 15 pages (10 pt font or larger, one or two columns, 1 inch margins, single or double spaced — more is not better). Your report should introduce and motivate the problem your project addresses, describe related work in the area, discuss the elements of your solution, and present results that measure the behavior, performance, or functionality of your system (with comparisons to other related systems as appropriate.)

Because this report is the primary deliverable upon which you will be graded, do not treat it as an afterthought . Plan to leave at least a week to do the writing, and make sure you proofread and edit carefully!

Please submit a paper copy of your report. You will also be expected to give a presentation on your project in class that will provide an opportunity for you to present a short demo of your work and show what you have done to other students in the class. Details about the format of the presentation will be posted as the date gets closer.

Project Ideas

The following is a list of possible project ideas; you are not required to choose from this list — in fact, we encourage you to try to solve a problem of your own choosing! If you are interested in working on one of these projects, contact the instructors and we can put you in touch with students and others around MIT working on these ideas. Note that these are not meant to be complete project proposals, but just suggestions for areas to explore — you will need to flesh them out into complete projects by talking with your group members, the course staff, and graduate students working on these projects.

Being able to compare performance of different DBMSs and different storage and access techniques is vital for the database community. To this purpose several synthetic benchmark has been designed and adopted over time (see TPC-C, TPC-H etc…). Wikipedia open source application, and publicly available data (several TB!!), provide a great starting point to develop a benchmark based on real-world data. Moreover, we obtained from the Wikimedia foundation 10% of 4 months of Wikipedia accesses (roughly 20 billion HTTP requests!). The project will consists in using this real-world data, queries and access patterns to design one of the first benchmarks based on real-world data.

Amazon RDS is a database service provided within the EC2 cloud. An interesting project consists in investigating performance and scalability characteristics of Amazon RDS. Also since RDS services run in a virtualized environment, studying the “stability” and “isolation” of the performance offered is interesting.

Hosted database services such as Amazon RDS, Microso SQL Azure are starting to become popular. It is still unclear what is the performance impact of running applications on a local (non-hosted) platform, such as a local enterprise datacenter, while having the data hosted “in the cloud”. An interesting project aim at investigating the performance impact for different classes of applications e.g., OLAP, OLTP, Web.

Performance monitoring is an important portion of data-center and database management. An interesting project consists in developing a monitoring interface for MySQL, capable of monitoring multiple nodes, reporting both DBMS internal statistics, and OS-level statistics (CPU, RAM, DIsk), potentially automating the detection of saturation of resources.

Being able to predict cpu/mem/disk load of database machines can enable “consolidation”, i.e., the co-location of multiple DB within a smaller set of physical servers. We have an interesting set of data from real-world data-centers, the project would consist in investigating machine-learning and other predictive techniques on such real-world data.

Flash memories are very promising technologies, providing lower latency for random operations. However, they have a series of unusual restrictions and performance. An interesting project investigates the performance impact of using flash memories for DB applications.

Often database assume data to be stored on a local disk, however data stored on network file systems can allow for easier administration, and is rather common in enterprises using SAN or NAS storage systems. The project will investigate the impact of local-vs-networked storage on query performance.

Partition-aware object-relational mapping. Many programmers seem to prefer object-relational mapping (ORM) layers such as like Ruby on Rails or Hibernate to a traditional ODBC/JDBC interface to a database. In the H-store Project we have been studying performance benefits that can be obtained in a “partitonable” database, where the tables can be cleanly partitioned according to some key attribute (for example, customer-id), and queries are generally run over just one partition. The goal of this project would be to study how to exploit partitioning to improve the performance of a distributed ORM layer.

Twitter provides a fire hose of data. Automatically filtering, aggregating, analyzing such data can allow a way to harness the full value of the data, extracting valuable information. The idea of this project is investigating stream processing technology to operate on social streams.

Client-side database. Build a Javascript library that client-side Web applications can use to access a database; the idea is to avoid the painful way in which current client-side application have to use the XMLHttpRequest interface to access server-side objects asynchronously. This layer should cache objects on the client side whenever possible, but be backed by a shared, server-side database system.

As a related project, HTML5 browsers (including WebKit, used by Safari and Chrome), include a client-side SQL API in JavaScript. This project would involve investigating how to user such a database to improve client performance, offload work from the server, etc.

Preventing denial-of-service attacks on database systems. Databases are a vulnerable point in many Web sites, because it is often possible for attackers to make some simple request that causes the Web site to issue queries asking the database to do a lot of work. By issuing a large number of such requests, and attacker can effectively issue a denial of service attack against the Web site by disabling the database. The goal of this project would be to develop a set of techniques to counter this problem — for example, one approach might be to modify the database scheduler so that it doesn’t run the same expensive queries over and over.

Auto-admin tools to recommend indices, etc. Design a tool that recommends a set of indices to build given a particular workload and a set of statistics in a database. Alternatively investigate the question of which materialized views to create in a data-warehousing system, such as

Scientific community data management requirements significantly differ from regular web/enterprise ones. To this purpose a specialized DB is currently being developed named: SciDB. Studying performance of SciDB on dedicated servers vs. on virtualized environment such as EC2 is an intriguing topic. Another interesting investigation would cover the impact on SciDB performance of storing the data over the network (e.g., network file system). A third interesting project would explore the performance of clustering algorithms on SciDB vs. MapReduce.

Asynchronous Database Access. Client software interacts with standard SQL databases via a blocking interface like ODBC or JDBC; the client sends SQL, waits for the database to process the query, and receives an answer. A non-blocking interface would allow a single client thread to issue many parallel queries from the same thread, with potential for some impressive performance gains. This project would investigate how this would work (do the queries have to be in different transactions? what kind of modification would need to be made to the database) and would look at the possible performance gains in some typical database benchmarks or applications.

Extend SimpleDB. SimpleDB is very simple. There are a number of ways you might extend it to explore some of the research ideas we have studied in this class. For example, you could add support for optimistic concurrency control and compare its performance to the basic concurrency control scheme you will implement in Problem Set 3. There are a number of other possible projects of this type; we would be happy to discuss these in more detail.

CarTel. In the CarTel project, we are building a system for collecting and managing data from automobiles. There are several possible CarTel related projects: * One of the features of CarTel is a GUI for browsing geo-spatial data collected from cars. We currently have a primitive interface for retrieving parts of the data that are of interest, but developing a more sophisticated interface or query language for browsing and exploring this data would make a great project. * One of the dangers with building a system like CarTel is that it collects relatively sensitive personal information about users location and driving habits. Protecting this information from casual browsers, insurance companies, or other undesired users is important. However, it is also important to be able to combine different users data together to do things like intelligent route planning or vehicle anomaly detection. The goal of this project would be to find a way to securely perform certain types of aggregate queries over CarTel data without exposing personally identifiable information. * We have speed and position data from the last year for 30 taxi cabs on the Boston streets. Think of something exciting you could do with this.

Rollback of long-running or committed transactions. Database systems typically only support UNDO of committed transactions, but there are cases where it might be important to rollback already committed transactions. One approach is to use user-supplied compensating actions, but there may be other models that are possible, or it may be possible to automatically derive such compensating action for certain classes of transactions.

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• Mastering Database Assignments: Your Comprehensive Guide

Navigating Database Assignments: A Step-by-Step Guide for Success

Embarking on the journey of database assignments is a dynamic venture that presents both challenges and rewards. Regardless of whether you find yourself navigating the academic realm as a student or seeking to elevate your professional expertise, this comprehensive guide serves as an invaluable companion throughout the entire process. From laying the groundwork by understanding fundamental concepts to the practical application of UML diagrams in database design, this guide is crafted to provide a seamless and insightful experience. As you progress, the guide will aid in deciphering complex assignment instructions, establishing a strategic framework, and delving into the principles of database design. With a spotlight on essential aspects like normalization techniques and relationship mapping, you'll gain a nuanced understanding of structuring databases for optimal performance. The journey further unfolds into the practical implementation phase, where you'll delve into the intricacies of writing SQL queries and employing data modeling techniques with tools like MySQL Workbench. The guide extends its support into troubleshooting common issues and optimizing database performance, ensuring a well-rounded comprehension of the entire database assignment landscape. Testing and validation, crucial components of the process, are explored extensively, emphasizing rigorous testing protocols and the importance of user feedback for iterative improvement. Whether you're a novice seeking to grasp the basics or a seasoned professional aiming to refine your skills, this guide is tailored to offer actionable insights and tips at each juncture. As you navigate the intricate world of database assignments, this guide stands as a beacon, illuminating the path to success with its comprehensive approach, ensuring that you emerge well-equipped and confident in your ability to tackle any database assignment that comes your way.

The guide encourages a proactive mindset, fostering an understanding that every database assignment is an opportunity for growth and skill refinement. It recognizes the importance of aligning theoretical knowledge with practical implementation, emphasizing the mastery of SQL queries, data modeling techniques, and troubleshooting strategies. By unraveling the complexities of common issues that may arise during assignments, such as schema errors and performance challenges, the guide empowers you to approach problem-solving with confidence and precision. Furthermore, it underscores the significance of performance optimization strategies, from indexing to query optimization, ensuring that your database not only meets the assignment requirements but operates at peak efficiency. As the journey concludes, the focus shifts to testing and validation, guiding you through a comprehensive testing strategy that encompasses unit testing, integration testing, and validation against real-world scenarios. The iterative improvement process is highlighted, recognizing the value of user feedback in refining your database design to meet evolving requirements.

Are you struggling to solve your Database homework ? This guide encapsulates the entire spectrum of navigating database assignments. Whether you are entering this realm with curiosity or experience, the guide serves as a reliable companion, providing practical wisdom and insights that transcend the theoretical. By the time you reach the conclusion, you'll find yourself well-versed in the intricacies of database assignments, armed with the knowledge to tackle challenges and contribute meaningfully to the dynamic field of database management. The journey, though demanding, is undeniably rewarding, and this comprehensive guide ensures that you traverse it with competence, resilience, and a deep understanding of the intricate world of databases.

Understanding the Basics

Before embarking on database assignments, establishing a robust foundation in database fundamentals is imperative. This involves delving into essential concepts like data models, relational databases, and normalization. A thorough grasp of these fundamentals not only facilitates a deeper understanding of database structures but also serves as the cornerstone for successful assignment completion. Additionally, recognizing the pivotal role of Unified Modeling Language (UML) diagrams is essential in the realm of database assignments. These diagrams, particularly entity-relationship diagrams (ERDs), hold significant weight in visualizing and conceptualizing database structures. Learning to create UML diagrams enables effective communication of database designs and contributes to a clearer representation of relationships among data entities. In essence, the synergy between comprehending database fundamentals and harnessing the power of UML diagrams sets the stage for a more informed and structured approach to tackling intricate database assignments.

Moreover, a nuanced understanding of data models lays the groundwork for effective communication between stakeholders involved in the assignment process. By comprehending the intricacies of relational databases, individuals can navigate the complexities of data storage and retrieval, essential components of any successful database assignment. The significance of normalization, a process to eliminate data redundancy and ensure data integrity, cannot be overstated. It establishes the guidelines for organizing data efficiently, contributing to the overall effectiveness of a database system.

Simultaneously, delving into the importance of UML diagrams unveils a visual language that transcends the limitations of text-based explanations. ERDs, a specific type of UML diagram, provide a graphical representation of entities and their relationships, offering a holistic view of the database structure. Proficiency in creating UML diagrams empowers individuals to convey complex database designs in a comprehensible manner, fostering collaboration and understanding among team members.

Analyzing Assignment Requirements

Embarking on a database assignment demands the twin capabilities of decoding assignment instructions and establishing a robust framework, both integral to ensuring triumph in the intricate landscape of database design. In the decoding phase, a meticulous breakdown of instructions is paramount – an analytical dissection where key requirements, constraints, and objectives are identified. This process, akin to deciphering a complex code, is the keystone for comprehending the assignment's scope, laying the foundation for subsequent strategic decisions. Simultaneously, the establishment of a framework involves the creation of a comprehensive roadmap. This entails defining the assignment's scope, functionalities, and data entities, fostering a structured approach that transforms the assignment into a navigable journey. These dual processes, decoding and establishing, synergistically shape the entire trajectory of the database assignment, guaranteeing not just completion, but success through clarity, coherence, and operational efficiency in every phase of the intricate database design process

Beyond being procedural necessities, decoding assignment instructions and establishing a framework serve as proactive measures that significantly impact the overall quality of the database solution. By meticulously decoding instructions, one gains a nuanced understanding of the assignment's nuances, fostering an awareness that goes beyond the surface requirements. This depth of comprehension becomes the fulcrum upon which creative and innovative solutions can be built. Similarly, the framework-setting phase is not merely a logistical exercise; it is a strategic endeavor that shapes the assignment's trajectory. The defined scope becomes a boundary for creativity, functionalities are crafted with purpose, and data entities are chosen with foresight. This intentional approach ensures that every subsequent step aligns with the overarching objectives, preventing missteps and ensuring a cohesive, well-integrated database design.

Moreover, the iterative nature of these processes becomes apparent as the assignment progresses. As challenges emerge, the initial decoding of instructions provides a reference point, enabling dynamic adjustments to the evolving understanding of the assignment. The established framework serves as a flexible guide, allowing for adaptations and refinements based on newfound insights or changing requirements. In essence, decoding instructions and establishing a framework are not isolated actions; they are continuous threads woven into the fabric of the entire database assignment lifecycle.

Database Design Principles

Delve into the critical aspects of database design with a focus on normalization techniques and relationship mapping. Normalization serves as a fundamental principle in eliminating data redundancy, promoting a well-organized database structure. In this section, gain insights into the various normal forms and learn how to apply them judiciously to enhance data integrity. Uncover the intricacies of relationship mapping, where you'll master the art of defining connections between entities. Understand the nuances of one-to-one, one-to-many, and many-to-many relationships, pivotal for designing a database that accurately mirrors real-world scenarios. This exploration ensures not only the efficiency of your database but also its alignment with the complexities of the environments it aims to represent.

As you navigate normalization, consider the journey towards a database that not only stores data but does so with optimal efficiency and accuracy. Normalization not only streamlines your data structure but also minimizes the chances of anomalies, ensuring that your database remains a reliable source of information. Additionally, the mastery of relationship mapping goes beyond theoretical knowledge, empowering you to translate real-world connections into a digital format seamlessly. By understanding the dynamics of different relationships, you pave the way for a database that not only functions well internally but also accurately represents the intricate web of connections found in diverse scenarios. This dual focus on normalization and relationship mapping is the cornerstone of building databases that stand the test of practical implementation and real-world demands.

Writing SQL Queries: Navigating the World of Structured Query Language (SQL)

Embark on an enriching journey into the dynamic realm of SQL (Structured Query Language), the heartbeat of seamless database interactions. This section serves as your comprehensive guide to mastering the intricacies of SQL, providing you with the adept skills needed to navigate databases with finesse. Beginning with the fundamentals of SELECT statements and advancing into the intricacies of JOIN operations, you will develop the proficiency required to retrieve and manipulate data with surgical precision. Beyond being a mere skill, SQL proficiency emerges as the bedrock of successful database management, bestowing upon you the power to unlock and leverage the full potential of data within your assignments. Embrace SQL mastery, and chart a course toward elevated excellence in the realm of database dynamics..

Data Modeling Techniques: Crafting Intuitive Database Designs

In this segment, we explore the art and science of practical data modeling techniques, transforming abstract ideas into tangible, well-designed databases. Employing tools such as MySQL Workbench or Oracle SQL Developer, you'll gain hands-on experience in visualizing your database architecture. Visualization is not only about creating aesthetically pleasing diagrams; it's a strategic approach to conceptualizing the relationships between data entities. As you delve into data modeling, you'll discover its pivotal role in ensuring your database design aligns seamlessly with user feedback and evolving project requirements. These techniques are the bedrock of creating databases that not only meet specifications but also adapt and evolve with the dynamic nature of real-world applications.

Troubleshooting and Optimization

Navigating the intricate realm of database assignments demands a meticulous understanding of potential challenges and the adept application of optimization strategies. Beyond merely identifying schema errors and performance bottlenecks, this section of the guide immerses you in a deeper exploration of solutions that go beyond the conventional. Gain insights into advanced indexing techniques that go beyond the basics, strategically enhancing the efficiency of your database. Uncover the art of query optimization, a nuanced skill that distinguishes a proficient database designer from the rest. By honing these techniques, you not only mitigate risks but also elevate the overall performance of your database to unprecedented levels.

This comprehensive approach not only addresses common issues but fosters a strategic mindset towards problem-solving. It emphasizes the symbiotic relationship between potential challenges and optimization, reinforcing your ability to design databases that stand resilient against the complexities of real-world scenarios. In mastering this segment, you not only troubleshoot effectively but cultivate an expertise that transcends the ordinary, positioning yourself as a proficient navigator in the ever-evolving landscape of database assignments.

Rigorous Testing Protocols

In the realm of database assignments, the significance of implementing rigorous testing protocols cannot be overstated. A robust testing strategy serves as the linchpin for ensuring the flawless functionality and unwavering reliability of your database. This involves a meticulous approach to various testing methodologies, including the critical steps of unit testing, where individual components are scrutinized for accuracy, integrity, and functionality. Integration testing becomes equally pivotal, examining the seamless interaction between different components to ensure their harmonious collaboration within the larger system. Yet, the testing journey doesn't conclude there; it extends to the validation against real-world scenarios, where the practical application of the database is scrutinized under diverse conditions. Each testing phase contributes to fortifying the database's resilience, identifying potential vulnerabilities, and refining its performance. In essence, a well-executed testing protocol is the bedrock upon which a robust and dependable database stands.

User Feedback and Iterative Improvement

The symbiotic relationship between user feedback and iterative improvement is a cornerstone in the evolutionary process of database assignments. Beyond the realms of coding and design, the incorporation of user perspectives and stakeholder insights becomes paramount. Gathering feedback from end-users provides invaluable insights into the usability and functionality of the database in real-world scenarios. This iterative approach extends beyond mere bug fixes; it entails a profound commitment to continuous improvement. Embracing a mindset that perceives each feedback loop as an opportunity for enhancement, database designers iterate on their designs accordingly. This iterative improvement process is not only about fixing issues but also about adapting to evolving requirements and expectations. It is a dynamic cycle where user feedback fuels design refinements, creating a symbiotic relationship that fosters an ever-improving user experience. In essence, the database becomes a living entity, evolving in response to the needs and experiences of those who interact with it.

In conclusion, successfully navigating the intricate landscape of database assignments demands a harmonious blend of theoretical acumen and hands-on practical skills. This step-by-step guide serves as a beacon, illuminating the path to confidently tackle assignments, thereby ensuring triumph in your ventures within the realm of databases. Offering a holistic perspective, this comprehensive guide delves into the depths of database assignments, guiding you seamlessly from the conceptualization phase to the intricacies of implementation.

Mastering the principles of database design is paramount in establishing a solid foundation for your assignments. It involves understanding data models, normalization techniques, and the art of relationship mapping. With these skills in your arsenal, you can create well-structured databases that accurately represent real-world scenarios, minimizing data redundancy and ensuring data integrity.

Equally important is the proficiency in SQL queries, a powerful language for interacting with databases. From crafting basic SELECT statements to executing complex JOIN operations, acquiring these skills empowers you to retrieve and manipulate data with precision. The guide further extends into the realm of practical implementation, introducing data modeling techniques using tools like MySQL Workbench or Oracle SQL Developer.

Troubleshooting and optimization strategies are indispensable components of the database journey. As you explore common issues and delve into performance optimization techniques, you gain the ability to identify and rectify challenges, ensuring the efficiency and responsiveness of your databases.

Testing and validation emerge as crucial steps in the database assignment lifecycle. Implementing rigorous testing protocols and soliciting user feedback allow you to refine and iterate on your designs. Embracing a continuous improvement mindset positions you to adapt to evolving requirements, contributing to a dynamic and resilient database system.

In essence, each assignment becomes more than a task—it transforms into an opportunity for skill refinement and meaningful contribution to the dynamic field of database management. Armed with this comprehensive guide, you not only navigate the complexities of database assignments but also elevate your academic and professional pursuits, leaving an indelible mark in the ever-evolving landscape of data management.

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Database Simple Assignment Work - Bachelors Level

It contains Normalization works, ER diagram construction and Database Queries.

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How to Write an Effective Report for Your Database Assignments

It's important to understand the fundamental tenet on which a well-written report stands before getting into the specifics of report writing for database assignments . A report is a formal, structured document that communicates information in a straightforward, succinct, and impartial way. Report writing is extremely valuable for both academic and professional contexts, particularly in fields like database management where thorough analysis and clear communication of difficult concepts are essential.

The Importance of Structuring Your Database Assignment Report:

A structured report is not just a convenience; it is essential. This requirement results from the characteristics of the database, which are complex, sophisticated, and loaded with layers. Every component of a database—tables, keys, queries, and relations—plays a crucial part in creating a solid structure and interconnects to do so.

The same meticulous organization that goes into creating a database assignment report should also go into writing it. The logic behind your database design will be reinforced, your understanding of the subject will be effectively communicated, and your reader will be able to easily follow your line of reasoning with the aid of a well-structured report.

An Overview of the Report Writing Process

Writing a database assignment report can be challenging, especially given its technical nature and need for accuracy. To reduce some of this stress and make the process less intimidating, break it down into a step-by-step procedure.

• Having read the assignment guidelines: The first and possibly most important step is to comprehend the assignment's requirements. Which problem statement is it? What kind of a database are you supposed to design or examine? Are there any particular methods or theories you must use? Before you start writing, you should have answers to these questions in hand.
• Construct the database: You can begin creating the database once you have a solid understanding of the requirements. In this step, tables are created, relationships are established, and queries are set up.
• Record the procedure: Ensure that your database design process is fully documented. For a comprehensive report, you will require this information.
• Examine the findings: You must conduct a results analysis after creating your database. The assignment's requirements are met by the database, right? What adjustments are required if not?
• Compose the report: You can now start writing your report since you have all the necessary information. Your report should have a title page, an introduction, a section on the methods, a section on the results, a discussion, a section on the conclusion, and references.

Let's examine each of these steps in more detail and see how you can create a fantastic database assignment report.

Understanding the Assignment Brief:

Understanding the assignment brief completely is the first step in writing a successful report. You can familiarize yourself with the problem statement, the requirements, and the constraints here.

The assignment's context and goal are typically stated in the problem statement. For instance, you might be asked to create a database for a university, a store, or a library. Understanding the problem statement will aid in deciding the type of data your database should store and the relationships between various data entities.

The assignment's specific requirements should then be taken into consideration. Do you have to use any particular data types? Is a specific database management system (DBMS) required? Are there any specific SQL queries you need to run? Take note of these specifications because they will direct the database design process.

Finally, take into account the assignment's restrictions. There might be a cap on the number of tables you can create, for example, or you might have to eschew certain database conventions like redundancy or consistency.

Designing the Database:

The next step is to design your database after you have understood the assignment brief. This entails defining the tables, determining the connections between the tables, and establishing the queries.

Consider which tables you need to create first. Entities in the real world are comparable to tables in a database. For instance, tables for books, authors, and borrowers might be required if you are building a database for a library.

Determine the attributes for each table next. An entity's attributes are its characteristics. Title, year of publication, and ISBN are examples of attributes for books. A borrower's name, address, and borrower ID are examples of attributes.

Keep in mind that every table needs a primary key—a distinctive identifier for each record in the table. The ISBN number, for instance, could act as the main key for the book table.

The next step is to establish the relationships between the tables you defined earlier in addition to their attributes. One-to-one, one-to-many, and many-to-many relationships are the three different types that can exist in a database. For effective database design, it is essential to comprehend these relationships and implement them correctly.

Set up the queries you must run on your database last. You can use queries to modify and retrieve data from your database. They are necessary to comprehend the operation of your database and to demonstrate its capabilities in your report.

Examining the Data:

It's time to evaluate the outcomes after you've designed and implemented your database. This entails executing your SQL queries and determining whether your database satisfies the assignment's requirements.

Determine the shortcomings of your database if it fails to deliver the results you were expecting. Do your tables require any changes? Should you make changes to your queries? Record these problems along with the steps you took to fix them.

The Report's Writing:

You're prepared to write your report now that you have all the data and analysis in front of you. A suggested format for your report is as follows:

• Name Page: Your name, the name of your instructor, the date, and the title of your report should all be on the title page.
• Introduction: An overview of the assignment's goals should be given in the introduction. Describe the problem statement and your database's objectives.
• Methods: Explain the procedure you used to create your database in this section. Include your ERD, describe how you went about normalizing your data, and give the SQL queries you employed. Make sure to articulate the thinking behind your design choices.
• Results: Display the database's findings here. Display the results of your SQL queries and explain what they mean.
• Discussion: Discuss your database's advantages and disadvantages. What difficulties did you experience, and how did you get through them? How well does your database meet the demands of the assignment?
• Conclusion: Summarize your report's main points. Think about the lessons you took away from the assignment and how you can use them to improve your database projects in the future.
• References: List any outside resources you used to complete your assignment if applicable. As per the requirements of your academic institution, make sure you use the proper citation style.

Keep in mind that a report with a solid structure shows that you understand the material and are organized. Spend some time checking your report for grammar mistakes and making sure the flow is proper. The reader will have an easier time understanding your work the better your report is.

Expanding on the Database Design Components:

Let's look into some of the key components of the database design process, which serves as the foundation for your assignment report.

ERDs are visual aids that show the organization of your database. Entities are depicted as rectangles, attributes as ovals, and relationships are represented as lines joining the rectangles. ERDs are essential because they make it easier to see your database's organizational structure and comprehend how various entities interact with one another.

Be exact and thorough when creating your ERD. Make certain that each entity, attribute, and relationship is mentioned. Your ERD should offer a thorough overview of your database, enabling anyone who reads your report to quickly comprehend its layout.

Another essential component of database design is normalization, which guarantees that your database is effective and free of redundancy. In order to remove redundancy and dependency, a database is split into two or more tables, with relationships between the tables being defined.

Your report should outline the normalization procedure and list the normal forms (1NF, 2NF, 3NF, and BCNF) attained. Explain the steps required to achieve each normal form and why it is important for your database design.

Since they let you manipulate and retrieve data, SQL queries are the engine that drives your database. The SQL queries you used to create the tables, insert data, update data, and retrieve data should all be included in your report.

It's also a good idea to describe each SQL query's purpose and methodology. This demonstrates your knowledge of SQL and how it can be used for database management.

Conclusion:

Writing a database assignment report is a challenging task, but it is definitely doable if you break it down into smaller steps. You'll be on the right track to passing your database assignment if you have a solid grasp of the assignment, a well-considered design, thorough documentation, and a well-structured report.

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Database design basics

A properly designed database provides you with access to up-to-date, accurate information. Because a correct design is essential to achieving your goals in working with a database, investing the time required to learn the principles of good design makes sense. In the end, you are much more likely to end up with a database that meets your needs and can easily accommodate change.

This article provides guidelines for planning a desktop database. You will learn how to decide what information you need, how to divide that information into the appropriate tables and columns, and how those tables relate to each other. You should read this article before you create your first desktop database.

In this article

Some database terms to know, what is good database design, the design process, determining the purpose of your database, finding and organizing the required information, dividing the information into tables, turning information items into columns, specifying primary keys, creating the table relationships, refining the design, applying the normalization rules.

Access organizes your information into tables : lists of rows and columns reminiscent of an accountant’s pad or a spreadsheet. In a simple database, you might have only one table. For most databases you will need more than one. For example, you might have a table that stores information about products, another table that stores information about orders, and another table with information about customers.

Each row is more correctly called a record , and each column, a field . A record is a meaningful and consistent way to combine information about something. A field is a single item of information — an item type that appears in every record. In the Products table, for instance, each row or record would hold information about one product. Each column or field holds some type of information about that product, such as its name or price.

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Certain principles guide the database design process. The first principle is that duplicate information (also called redundant data) is bad, because it wastes space and increases the likelihood of errors and inconsistencies. The second principle is that the correctness and completeness of information is important. If your database contains incorrect information, any reports that pull information from the database will also contain incorrect information. As a result, any decisions you make that are based on those reports will then be misinformed.

A good database design is, therefore, one that:

Divides your information into subject-based tables to reduce redundant data.

Provides Access with the information it requires to join the information in the tables together as needed.

Helps support and ensure the accuracy and integrity of your information.

Accommodates your data processing and reporting needs.

The design process consists of the following steps:

Determine the purpose of your database     

This helps prepare you for the remaining steps.

Find and organize the information required     

Gather all of the types of information you might want to record in the database, such as product name and order number.

Divide the information into tables     

Divide your information items into major entities or subjects, such as Products or Orders. Each subject then becomes a table.

Turn information items into columns     

Decide what information you want to store in each table. Each item becomes a field, and is displayed as a column in the table. For example, an Employees table might include fields such as Last Name and Hire Date.

Specify primary keys     

Choose each table’s primary key. The primary key is a column that is used to uniquely identify each row. An example might be Product ID or Order ID.

Set up the table relationships     

Look at each table and decide how the data in one table is related to the data in other tables. Add fields to tables or create new tables to clarify the relationships, as necessary.

Refine your design     

Analyze your design for errors. Create the tables and add a few records of sample data. See if you can get the results you want from your tables. Make adjustments to the design, as needed.

Apply the normalization rules     

Apply the data normalization rules to see if your tables are structured correctly. Make adjustments to the tables, as needed.

It is a good idea to write down the purpose of the database on paper — its purpose, how you expect to use it, and who will use it. For a small database for a home based business, for example, you might write something simple like "The customer database keeps a list of customer information for the purpose of producing mailings and reports." If the database is more complex or is used by many people, as often occurs in a corporate setting, the purpose could easily be a paragraph or more and should include when and how each person will use the database. The idea is to have a well developed mission statement that can be referred to throughout the design process. Having such a statement helps you focus on your goals when you make decisions.

To find and organize the information required, start with your existing information. For example, you might record purchase orders in a ledger or keep customer information on paper forms in a file cabinet. Gather those documents and list each type of information shown (for example, each box that you fill in on a form). If you don't have any existing forms, imagine instead that you have to design a form to record the customer information. What information would you put on the form? What fill-in boxes would you create? Identify and list each of these items. For example, suppose you currently keep the customer list on index cards. Examining these cards might show that each card holds a customers name, address, city, state, postal code and telephone number. Each of these items represents a potential column in a table.

As you prepare this list, don’t worry about getting it perfect at first. Instead, list each item that comes to mind. If someone else will be using the database, ask for their ideas, too. You can fine-tune the list later.

Next, consider the types of reports or mailings you might want to produce from the database. For instance, you might want a product sales report to show sales by region, or an inventory summary report that shows product inventory levels. You might also want to generate form letters to send to customers that announces a sale event or offers a premium. Design the report in your mind, and imagine what it would look like. What information would you place on the report? List each item. Do the same for the form letter and for any other report you anticipate creating.

Giving thought to the reports and mailings you might want to create helps you identify items you will need in your database. For example, suppose you give customers the opportunity to opt in to (or out of) periodic e-mail updates, and you want to print a listing of those who have opted in. To record that information, you add a “Send e-mail” column to the customer table. For each customer, you can set the field to Yes or No.

The requirement to send e-mail messages to customers suggests another item to record. Once you know that a customer wants to receive e-mail messages, you will also need to know the e-mail address to which to send them. Therefore you need to record an e-mail address for each customer.

It makes good sense to construct a prototype of each report or output listing and consider what items you will need to produce the report. For instance, when you examine a form letter, a few things might come to mind. If you want to include a proper salutation — for example, the "Mr.", "Mrs." or "Ms." string that starts a greeting, you will have to create a salutation item. Also, you might typically start a letter with “Dear Mr. Smith”, rather than “Dear. Mr. Sylvester Smith”. This suggests you would typically want to store the last name separate from the first name.

A key point to remember is that you should break each piece of information into its smallest useful parts. In the case of a name, to make the last name readily available, you will break the name into two parts — First Name and Last Name. To sort a report by last name, for example, it helps to have the customer's last name stored separately. In general, if you want to sort, search, calculate, or report based on an item of information, you should put that item in its own field.

Think about the questions you might want the database to answer. For instance, how many sales of your featured product did you close last month? Where do your best customers live? Who is the supplier for your best-selling product? Anticipating these questions helps you zero in on additional items to record.

After gathering this information, you are ready for the next step.

To divide the information into tables, choose the major entities, or subjects. For example, after finding and organizing information for a product sales database, the preliminary list might look like this:

The major entities shown here are the products, the suppliers, the customers, and the orders. Therefore, it makes sense to start out with these four tables: one for facts about products, one for facts about suppliers, one for facts about customers, and one for facts about orders. Although this doesn’t complete the list, it is a good starting point. You can continue to refine this list until you have a design that works well.

When you first review the preliminary list of items, you might be tempted to place them all in a single table, instead of the four shown in the preceding illustration. You will learn here why that is a bad idea. Consider for a moment, the table shown here:

In this case, each row contains information about both the product and its supplier. Because you can have many products from the same supplier, the supplier name and address information has to be repeated many times. This wastes disk space. Recording the supplier information only once in a separate Suppliers table, and then linking that table to the Products table, is a much better solution.

A second problem with this design comes about when you need to modify information about the supplier. For example, suppose you need to change a supplier's address. Because it appears in many places, you might accidentally change the address in one place but forget to change it in the others. Recording the supplier’s address in only one place solves the problem.

When you design your database, always try to record each fact just once. If you find yourself repeating the same information in more than one place, such as the address for a particular supplier, place that information in a separate table.

Finally, suppose there is only one product supplied by Coho Winery, and you want to delete the product, but retain the supplier name and address information. How would you delete the product record without also losing the supplier information? You can't. Because each record contains facts about a product, as well as facts about a supplier, you cannot delete one without deleting the other. To keep these facts separate, you must split the one table into two: one table for product information, and another table for supplier information. Deleting a product record should delete only the facts about the product, not the facts about the supplier.

Once you have chosen the subject that is represented by a table, columns in that table should store facts only about the subject. For instance, the product table should store facts only about products. Because the supplier address is a fact about the supplier, and not a fact about the product, it belongs in the supplier table.

To determine the columns in a table, decide what information you need to track about the subject recorded in the table. For example, for the Customers table, Name, Address, City-State-Zip, Send e-mail, Salutation and E-mail address comprise a good starting list of columns. Each record in the table contains the same set of columns, so you can store Name, Address, City-State-Zip, Send e-mail, Salutation and E-mail address information for each record. For example, the address column contains customers’ addresses. Each record contains data about one customer, and the address field contains the address for that customer.

Once you have determined the initial set of columns for each table, you can further refine the columns. For example, it makes sense to store the customer name as two separate columns: first name and last name, so that you can sort, search, and index on just those columns. Similarly, the address actually consists of five separate components, address, city, state, postal code, and country/region, and it also makes sense to store them in separate columns. If you want to perform a search, filter or sort operation by state, for example, you need the state information stored in a separate column.

You should also consider whether the database will hold information that is of domestic origin only, or international, as well. For instance, if you plan to store international addresses, it is better to have a Region column instead of State, because such a column can accommodate both domestic states and the regions of other countries/regions. Similarly, Postal Code makes more sense than Zip Code if you are going to store international addresses.

The following list shows a few tips for determining your columns.

Don’t include calculated data     

In most cases, you should not store the result of calculations in tables. Instead, you can have Access perform the calculations when you want to see the result. For example, suppose there is a Products On Order report that displays the subtotal of units on order for each category of product in the database. However, there is no Units On Order subtotal column in any table. Instead, the Products table includes a Units On Order column that stores the units on order for each product. Using that data, Access calculates the subtotal each time you print the report. The subtotal itself should not be stored in a table.

Store information in its smallest logical parts     

You may be tempted to have a single field for full names, or for product names along with product descriptions. If you combine more than one kind of information in a field, it is difficult to retrieve individual facts later. Try to break down information into logical parts; for example, create separate fields for first and last name, or for product name, category, and description.

Once you have refined the data columns in each table, you are ready to choose each table's primary key.

Each table should include a column or set of columns that uniquely identifies each row stored in the table. This is often a unique identification number, such as an employee ID number or a serial number. In database terminology, this information is called the primary key of the table. Access uses primary key fields to quickly associate data from multiple tables and bring the data together for you.

If you already have a unique identifier for a table, such as a product number that uniquely identifies each product in your catalog, you can use that identifier as the table’s primary key — but only if the values in this column will always be different for each record. You cannot have duplicate values in a primary key. For example, don’t use people’s names as a primary key, because names are not unique. You could easily have two people with the same name in the same table.

A primary key must always have a value. If a column's value can become unassigned or unknown (a missing value) at some point, it can't be used as a component in a primary key.

You should always choose a primary key whose value will not change. In a database that uses more than one table, a table’s primary key can be used as a reference in other tables. If the primary key changes, the change must also be applied everywhere the key is referenced. Using a primary key that will not change reduces the chance that the primary key might become out of sync with other tables that reference it.

Often, an arbitrary unique number is used as the primary key. For example, you might assign each order a unique order number. The order number's only purpose is to identify an order. Once assigned, it never changes.

If you don’t have in mind a column or set of columns that might make a good primary key, consider using a column that has the AutoNumber data type. When you use the AutoNumber data type, Access automatically assigns a value for you. Such an identifier is factless; it contains no factual information describing the row that it represents. Factless identifiers are ideal for use as a primary key because they do not change. A primary key that contains facts about a row — a telephone number or a customer name, for example — is more likely to change, because the factual information itself might change.

1. A column set to the AutoNumber data type often makes a good primary key. No two product IDs are the same.

In some cases, you may want to use two or more fields that, together, provide the primary key of a table. For example, an Order Details table that stores line items for orders would use two columns in its primary key: Order ID and Product ID. When a primary key employs more than one column, it is also called a composite key.

For the product sales database, you can create an AutoNumber column for each of the tables to serve as primary key: ProductID for the Products table, OrderID for the Orders table, CustomerID for the Customers table, and SupplierID for the Suppliers table.

Now that you have divided your information into tables, you need a way to bring the information together again in meaningful ways. For example, the following form includes information from several tables.

1. Information in this form comes from the Customers table...

2. ...the Employees table...

3. ...the Orders table...

4. ...the Products table...

5. ...and the Order Details table.

Access is a relational database management system. In a relational database, you divide your information into separate, subject-based tables. You then use table relationships to bring the information together as needed.

Creating a one-to-many relationship

Consider this example: the Suppliers and Products tables in the product orders database. A supplier can supply any number of products. It follows that for any supplier represented in the Suppliers table, there can be many products represented in the Products table. The relationship between the Suppliers table and the Products table is, therefore, a one-to-many relationship.

To represent a one-to-many relationship in your database design, take the primary key on the "one" side of the relationship and add it as an additional column or columns to the table on the "many" side of the relationship. In this case, for example, you add the Supplier ID column from the Suppliers table to the Products table. Access can then use the supplier ID number in the Products table to locate the correct supplier for each product.

The Supplier ID column in the Products table is called a foreign key. A foreign key is another table’s primary key. The Supplier ID column in the Products table is a foreign key because it is also the primary key in the Suppliers table.

You provide the basis for joining related tables by establishing pairings of primary keys and foreign keys. If you are not sure which tables should share a common column, identifying a one-to-many relationship ensures that the two tables involved will, indeed, require a shared column.

Creating a many-to-many relationship

Consider the relationship between the Products table and Orders table.

A single order can include more than one product. On the other hand, a single product can appear on many orders. Therefore, for each record in the Orders table, there can be many records in the Products table. And for each record in the Products table, there can be many records in the Orders table. This type of relationship is called a many-to-many relationship because for any product, there can be many orders; and for any order, there can be many products. Note that to detect many-to-many relationships between your tables, it is important that you consider both sides of the relationship.

The subjects of the two tables — orders and products — have a many-to-many relationship. This presents a problem. To understand the problem, imagine what would happen if you tried to create the relationship between the two tables by adding the Product ID field to the Orders table. To have more than one product per order, you need more than one record in the Orders table per order. You would be repeating order information for each row that relates to a single order — resulting in an inefficient design that could lead to inaccurate data. You run into the same problem if you put the Order ID field in the Products table — you would have more than one record in the Products table for each product. How do you solve this problem?

The answer is to create a third table, often called a junction table, that breaks down the many-to-many relationship into two one-to-many relationships. You insert the primary key from each of the two tables into the third table. As a result, the third table records each occurrence or instance of the relationship.

Each record in the Order Details table represents one line item on an order. The Order Details table’s primary key consists of two fields — the foreign keys from the Orders and the Products tables. Using the Order ID field alone doesn’t work as the primary key for this table, because one order can have many line items. The Order ID is repeated for each line item on an order, so the field doesn’t contain unique values. Using the Product ID field alone doesn’t work either, because one product can appear on many different orders. But together, the two fields always produce a unique value for each record.

In the product sales database, the Orders table and the Products table are not related to each other directly. Instead, they are related indirectly through the Order Details table. The many-to-many relationship between orders and products is represented in the database by using two one-to-many relationships:

The Orders table and Order Details table have a one-to-many relationship. Each order can have more than one line item, but each line item is connected to only one order.

The Products table and Order Details table have a one-to-many relationship. Each product can have many line items associated with it, but each line item refers to only one product.

From the Order Details table, you can determine all of the products on a particular order. You can also determine all of the orders for a particular product.

After incorporating the Order Details table, the list of tables and fields might look something like this:

Creating a one-to-one relationship

Another type of relationship is the one-to-one relationship. For instance, suppose you need to record some special supplementary product information that you will need rarely or that only applies to a few products. Because you don't need the information often, and because storing the information in the Products table would result in empty space for every product to which it doesn’t apply, you place it in a separate table. Like the Products table, you use the ProductID as the primary key. The relationship between this supplemental table and the Product table is a one-to-one relationship. For each record in the Product table, there exists a single matching record in the supplemental table. When you do identify such a relationship, both tables must share a common field.

When you detect the need for a one-to-one relationship in your database, consider whether you can put the information from the two tables together in one table. If you don’t want to do that for some reason, perhaps because it would result in a lot of empty space, the following list shows how you would represent the relationship in your design:

If the two tables have the same subject, you can probably set up the relationship by using the same primary key in both tables.

If the two tables have different subjects with different primary keys, choose one of the tables (either one) and insert its primary key in the other table as a foreign key.

Determining the relationships between tables helps you ensure that you have the right tables and columns. When a one-to-one or one-to-many relationship exists, the tables involved need to share a common column or columns. When a many-to-many relationship exists, a third table is needed to represent the relationship.

Once you have the tables, fields, and relationships you need, you should create and populate your tables with sample data and try working with the information: creating queries, adding new records, and so on. Doing this helps highlight potential problems — for example, you might need to add a column that you forgot to insert during your design phase, or you may have a table that you should split into two tables to remove duplication.

See if you can use the database to get the answers you want. Create rough drafts of your forms and reports and see if they show the data you expect. Look for unnecessary duplication of data and, when you find any, alter your design to eliminate it.

As you try out your initial database, you will probably discover room for improvement. Here are a few things to check for:

Did you forget any columns? If so, does the information belong in the existing tables? If it is information about something else, you may need to create another table. Create a column for every information item you need to track. If the information can’t be calculated from other columns, it is likely that you will need a new column for it.

Are any columns unnecessary because they can be calculated from existing fields? If an information item can be calculated from other existing columns — a discounted price calculated from the retail price, for example — it is usually better to do just that, and avoid creating new column.

Are you repeatedly entering duplicate information in one of your tables? If so, you probably need to divide the table into two tables that have a one-to-many relationship.

Do you have tables with many fields, a limited number of records, and many empty fields in individual records? If so, think about redesigning the table so it has fewer fields and more records.

Has each information item been broken into its smallest useful parts? If you need to report, sort, search, or calculate on an item of information, put that item in its own column.

Does each column contain a fact about the table's subject? If a column does not contain information about the table's subject, it belongs in a different table.

Are all relationships between tables represented, either by common fields or by a third table? One-to-one and one-to- many relationships require common columns. Many-to-many relationships require a third table.

Refining the Products table

Suppose that each product in the product sales database falls under a general category, such as beverages, condiments, or seafood. The Products table could include a field that shows the category of each product.

Suppose that after examining and refining the design of the database, you decide to store a description of the category along with its name. If you add a Category Description field to the Products table, you have to repeat each category description for each product that falls under the category — this is not a good solution.

A better solution is to make Categories a new subject for the database to track, with its own table and its own primary key. You can then add the primary key from the Categories table to the Products table as a foreign key.

The Categories and Products tables have a one-to-many relationship: a category can include more than one product, but a product can belong to only one category.

When you review your table structures, be on the lookout for repeating groups. For example, consider a table containing the following columns:

Product ID1

Product ID2

Product ID3

Here, each product is a repeating group of columns that differs from the others only by adding a number to the end of the column name. When you see columns numbered this way, you should revisit your design.

Such a design has several flaws. For starters, it forces you to place an upper limit on the number of products. As soon as you exceed that limit, you must add a new group of columns to the table structure, which is a major administrative task.

Another problem is that those suppliers that have fewer than the maximum number of products will waste some space, since the additional columns will be blank. The most serious flaw with such a design is that it makes many tasks difficult to perform, such as sorting or indexing the table by product ID or name.

Whenever you see repeating groups review the design closely with an eye on splitting the table in two. In the above example it is better to use two tables, one for suppliers and one for products, linked by supplier ID.

You can apply the data normalization rules (sometimes just called normalization rules) as the next step in your design. You use these rules to see if your tables are structured correctly. The process of applying the rules to your database design is called normalizing the database, or just normalization.

Normalization is most useful after you have represented all of the information items and have arrived at a preliminary design. The idea is to help you ensure that you have divided your information items into the appropriate tables. What normalization cannot do is ensure that you have all the correct data items to begin with.

You apply the rules in succession, at each step ensuring that your design arrives at one of what is known as the "normal forms." Five normal forms are widely accepted — the first normal form through the fifth normal form. This article expands on the first three, because they are all that is required for the majority of database designs.

First normal form

First normal form states that at every row and column intersection in the table there, exists a single value, and never a list of values. For example, you cannot have a field named Price in which you place more than one Price. If you think of each intersection of rows and columns as a cell, each cell can hold only one value.

Second normal form

Second normal form requires that each non-key column be fully dependent on the entire primary key, not on just part of the key. This rule applies when you have a primary key that consists of more than one column. For example, suppose you have a table containing the following columns, where Order ID and Product ID form the primary key:

Order ID (primary key)

Product ID (primary key)

Product Name

This design violates second normal form, because Product Name is dependent on Product ID, but not on Order ID, so it is not dependent on the entire primary key. You must remove Product Name from the table. It belongs in a different table (Products).

Third normal form

Third normal form requires that not only every non-key column be dependent on the entire primary key, but that non-key columns be independent of each other.

Another way of saying this is that each non-key column must be dependent on the primary key and nothing but the primary key. For example, suppose you have a table containing the following columns:

ProductID (primary key)

Assume that Discount depends on the suggested retail price (SRP). This table violates third normal form because a non-key column, Discount, depends on another non-key column, SRP. Column independence means that you should be able to change any non-key column without affecting any other column. If you change a value in the SRP field, the Discount would change accordingly, thus violating that rule. In this case Discount should be moved to another table that is keyed on SRP.

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Home » Getting Started with MySQL » MySQL Sample Database

MySQL Sample Database

We use the classicmodels database as a MySQL sample database to help you work with MySQL quickly and effectively.

The classicmodels database is a retailer of scale models of classic cars. It contains typical business data, including information about customers, products, sales orders, sales order line items, and more.

We’ll use this sample database in our MySQL tutorials to demonstrate a wide range of MySQL features, from simple queries to complex stored procedures .

Download MySQL Sample Database

You can download the sample database from the following link:

The download file is in ZIP format, so you’ll need a zip program to unzip it. You can download a free zip program at www.7-zip.org .

After uncompressing the sampledatabase.zip file, you can load the sample database into the MySQL database server by following the tutorial on how to do so.

MySQL Sample Database Schema

The MySQL sample database schema consists of the following tables:

• customers : stores customer’s data.
• products : stores a list of scale model cars.
• productlines : stores a list of product lines.
• orders : stores sales orders placed by customers.
• orderdetails : stores sales order line items for every sales order.
• payments : stores payments made by customers based on their accounts.
• employees : stores employee information and the organization structure such as who reports to whom.
• offices : stores sales office data.

The following picture illustrates the ER diagram of the sample database:

You can download the MySQL sample database ER diagram in PDF format from the following link:

Download MySQL Sample Database Diagram PDF A4

We recommend printing out the ER diagram and placing it on your desk to help you become familiar with the schema as you learn MySQL.

Have fun learning MySQL!

MySQL Practice: Best Exercises for Beginners

• online practice

Table of Contents

A Note on Our MySQL Practice Exercises

Dataset: cats, exercise 1: list all cats, exercise 2: select younger cats, exercise 3: list all ragdoll cats, exercise 4: select cats whose favorite toy is a ball, exercise 5: find older cats with a favorite toy, dataset: games, exercise 6: order data by cost and rating, exercise 7: order high-rated games by production cost, exercise 8: count games produced per year, exercise 9: count profitable games by type and company, exercise 10: list the number of games and average cost, dataset: employees, exercise 11: list salary grades for all employees, exercise 13: list all benefits for employee 5, exercise 12: show employees and direct managers, exercise 14: show benefits and how many employees receive them, exercise 15: show benefits not received by any employee, embrace the power of mysql practice exercises.

These 15 MySQL exercises are especially designed for beginners. They cover essential topics like selecting data from one table, ordering and grouping data, and joining data from multiple tables

This article showcases beginner-level MySQL practice exercises, including solutions and comprehensive explanations. If you need to practice …

• Selecting data from one table
• Ordering and grouping data
• Joining data from multiple tables

… these 15 tasks are just for you!

Most of the exercises come from our SQL Practice Set in MySQL , which provides a complete training environment where you can run queries and see the output data. Upon enrolling in this course, you’ll have access to 88 interactive beginner-friendly exercises to get you started towards SQL mastery.

While doing the exercises in this article, you may find this MySQL Cheat Sheet helpful. It contains a quick reference guide for MySQL syntax.

Continuous practice makes perfect, so the only way to learn and master your SQL skills is through practice. Following and solving practice exercises is the key to perfecting your SQL skills.

Let’s get started.

The exercises in the following sections use different datasets and cover distinct topics, including selecting data from one table, ordering and grouping data, and joining data from multiple tables.

Each section contains five exercises that include the following:

• The text of the exercise, which describes what data to fetch from the database.
• The solution query.
• A discussion on how the solution works and how the query was constructed.

We encourage you to solve the exercise on your own before looking at the solution. If you like these exercises, sign up for our SQL Practice Set in MySQL course and solve all 88 exercises!

Section 1: Selecting Data from One Table

We’ll start with the most basic SQL syntax, which is the SELECT statement. It selects data from a single table. You can use the WHERE clause to filter data based on defined conditions.

Let’s introduce the dataset and get to the MySQL practice exercises.

In this section, we’ll use the cat table. It consists of the following columns:

• id – The ID number of a given cat.
• name – The cat’s name.
• breed – The cat’s breed (e.g. Siamese, Ragdoll, etc.).
• coloration – The cat’s coloration.
• age – The cat’s age.
• sex – The cat’s sex.
• fav_toy – The cat’s favorite toy.

Select all data from the cat table.

Explanation:

We use the SELECT statement to define the columns to be outputted by the query. Here, we want to select all columns, so we use the asterisk character ( * ), which stands for all columns.

The FROM clause specifies the name of the table from which data is extracted.

It works just like we’d say it – we select all the columns from the cat table.

Select the name, breed, and coloration for every cat that is younger than five years old.

Again we use the SELECT statement to define the columns to be output by the query – here, the name , breed , and coloration columns.

It is followed by the FROM clause, which tells the database to get the data from the cat table.

Finally, we define the condition in the WHERE clause. As we want to select only cats that are younger than five years old, we impose a condition on the age column to be less than 5: WHERE age < 5 .

Select the ID and name for every cat that is of the Ragdoll breed.

We select the id and name columns from the cat table.

Next, we define a condition in the WHERE clause. As we want to list all cats of the Ragdoll breed, we impose a condition on the breed column to be equal to Ragdoll: WHERE breed = 'Ragdoll' . Note that in SQL we must enclose text strings in single quotation marks.

Select all data for cats whose:

• Breed starts with an 'R'.
• Favorite toy starts with the word 'ball'.
• Coloration name ends with an 'm'.

We use the SELECT statement to select all data. The asterisk character ( * ) stands for all columns. Therefore, SELECT * selects all columns available in the cat table, which is defined in the FROM clause.

Here, we define multiple conditions on different columns. We use the percentage character ( % ), which is a wildcard that stands for any sequence of characters.

• The breed column value must start with an 'R' followed by any sequence of characters: breed LIKE 'R%' .
• The fav_toy column value must start with the word 'ball' followed by any sequence of characters: fav_toy LIKE 'ball%' .
• The coloration column value must end with an 'm' preceded by any sequence of characters: coloration LIKE '%m' .

Since all these conditions must be applied at the same time, we combine them in the WHERE clause using the AND operator. This ensures that all conditions hold true for the selected data rows.

Select all data for cats that are:

• More than 10 years old.
• Either of the Ragdoll or Abyssinian breeds.
• Have a known favorite toy.

Again we select all columns from the cat table using an asterisk character ( * ) listed along the SELECT statement. Then we impose the conditions on the columns:

• We want to select cats that are older than 10, so we impose a condition on the age column: age > 10 .
• We want to select cats that are of Ragdoll or Abyssinian breeds, so we impose a condition on the breed column: ( breed = 'Ragdoll' OR breed = 'Abyssinian' ). Please note that this is a composite condition and we must enclose it in parentheses. The two parts are combined with the OR operator because we want to select either Ragdoll or Abyssinian cats.
• We want to select cats that have a favorite toy. Therefore, we impose a condition on the fav_toy column: fav_toy IS NOT NULL . We use the IS NOT NULL expression to ensure that selected cats have a favorite toy assigned.

To apply all three conditions at the same time, we combine them using the AND operator.

Section 2: Ordering and Grouping Data

Now that we’ve recalled how to query data from a table and filter it by imposing different conditions, let’s move on to ordering, aggregating, and grouping data.

In this section, we’ll use the games table, which has the following columns:

• id – The ID of a given game.
• title – The game’s title.
• company – The name of the company that produced the game.
• type – The game’s genre.
• production_year – The year when the game was created.
• system – The system on which the game is played.
• production_cost – The cost of producing this game.
• revenue – The revenue generated by the game.
• rating – The game’s

Select all data from the games table and order the results by the production cost from cheapest to most expensive. If multiple games have the same production cost, order the results by ratings, from best to worst.

We select all columns from the games table: SELECT * FROM games .

Next, we order the output data using the ORDER BY clause and the production_cost column. As we want it to be in ascending order, we follow the column name with the ASC keyword: ORDER BY production_cost ASC .

The secondary column by which we want to order the output data is the rating column. To order the data from best to worst rating (that is, in descending order), we use the DESC keyword: rating DESC .

Show the production cost values of games that were produced between 2010 and 2015 and were rated higher than 7. Order data by the cheapest to the most expensive production cost.

We select the production_cost column from the games table.

As we want to select only games produced between 2010 and 2015 and rated higher than 7, we impose conditions on the production_year and rating columns and combine them with the AND operator: production_year BETWEEN 2010 AND 2015 AND rating > 7 .

Finally, we order the output data by the production_cost column. We add the ASC keyword (which stands for ascending)  to order the data from cheapest to most expensive production costs.

For each year, display:

• The year ( production_year ).
• How many games were released in this year (as the count column).
• The average production costs per game for that year (as the avg_cost column).
• The average revenue per game for that year (as the avg_revenue column).

We select the following from the games table:

• The production_year
• The count of all games produced in that We use the COUNT() aggregate function , passing an asterisk ( * ) as its argument, to count all rows.
• The average production cost in that We use the AVG() aggregate function, passing the production_cost column as its argument, to calculate the average production cost.
• The average revenue within a given year. We use the AVG() aggregate function, passing the revenue column as its argument, to calculate the average revenue.

As we want to calculate these statistics (count and average) for each year, we need to group all data by the production_year column. We use the GROUP BY clause, passing production_year as its argument, so the data is put into as many groups as there are distinct values in the production_year column.

Count how many games of a given type and produced by a given company turned out to be profitable (their revenue was greater than their production cost). Show the number of games (as number_of_games ) and the company and type columns.

We select the company and type columns and the count of all games of each type produced by a given company. Therefore, we must list both the company and type columns in the GROUP BY clause so the groups are based on combinations of unique values from the company and type columns.

As we only need the profitable games, we impose a condition in the WHERE clause that ensures the revenue column value is greater than the production_cost column value. If that is not the case, this game will not be counted.

For each company, select its name, the number of games it produced (as the number_of_games column) and the average cost of production (as the avg_cost column). Show only companies that produced more than one game.

• The company name.
• The number of games produced by the company using the COUNT() aggregate function. Please note that you can use either the company column or an asterisk character (*) as an argument to COUNT() . The difference is that COUNT(*) counts all rows and COUNT(company) counts all rows where the company column is not null. In this case, the company column stores values for each row, so we can use the two options interchangeably. In most other queries, COUNT(column) and COUNT(*) are not interchangeable.
• The average production cost per company, using the AVG() aggregate function. Here, we pass the production_cost column as an argument to AVG() because we want to calculate an average for the values stored in this column.

As the SELECT statement lists one column ( company ) and two aggregate functions that perform calculations per company, we must group the output data by the company column.

To show only the companies that produced more than one game, we must impose a condition on the number_of_games column. However, we cannot use the WHERE clause because number_of_games is an alias name for the value calculated using the COUNT(company) aggregate function.

We use the HAVING clause to impose conditions on aggregate functions . This clause is processed after the GROUP BY clause. You can use either the alias name ( HAVING number_of_games > 1 ) or the aggregate function itself ( HAVING COUNT(company) > 1 ) to create such a condition.

Section 3: Joining Data from Multiple Tables

Now that we’ve reviewed how to query, filter, order, and group data from a single table, it’s time to move on to joining data from multiple tables and performing operations on joined data.

In this section, we’ll use the employees dataset that consists of the following tables:

• id – The ID of a given employee.
• first_name – The employee’s first name.
• last_name – The employee’s last name.
• salary – The employee’s salary.
• manager_id – The ID of this employee's manager.
• grade – The salary grade.
• lower_limit – This grade’s lower limit.
• upper_limit – This grade’s upper limit.
• salary_req – The minimum salary required to obtain a given benefit.
• benefit_name – The benefit name.

Select the first name, last name, salary, and salary grade of employees whose salary fits between the lower_limit and upper_limit from the salgrade table.

We want to select columns stored in the employee and salgrade tables. Therefore, we must join these two tables – or, as we do in this case, list them both in the FROM clause.

We select the first and last name and the salary from the employee table, and the grade column from the salgrade table. We list both these tables in the FROM clause.

We provide the join condition in the WHERE clause. The salary value from the employee table must fall between the lower_limit value and the upper_limit value from the salgrade table.

Show all benefits received by the employee with id = 5 . Select the first and last name of that employee and the benefits' names.

We select the first and last name from the employee table and the name of the benefit from the benefits table. Therefore, we must join the two tables using the JOIN statement.

To qualify for a certain benefit, the employee’s salary (stored in the salary column of the employee table) must be greater than or equal to the salary value required to obtain this benefit (stored in the salary_req column of the benefits table). Therefore, the join condition is exactly that: employee.salary >= benefits.salary_req .

As we want to show all benefits for the employee with id = 5 , we impose this condition in the WHERE clause.

Show each employee's first name, last name, salary, and the first name and last name of their direct manager in the same row. List only employees who have a direct manager.

Each record in the employee table contains a manager_id value that points to the id column of another employee who is this employee’s direct manager. Therefore, we must join the employee table with itself – i.e. a self-join.

The employee table aliased as e is used to select employees’ first name, last name, and salary. The employee table aliased as m is used to select the first and last name of the managers. This is defined in the ON clause, where we impose a join condition saying that the manager_id column from the e table must be equal to the id column from the m table. In effect, this makes the m table store managers of employees stored in the e table.

For each benefit, find the number of employees that receive it. Show two columns: the benefit_name and the count (name that column employee_count ). Don't show benefits that aren't received by anyone.

We want to count how many employees receive a given benefit. Therefore, we must join the benefits and employee tables.

To qualify for a certain benefit, the employee’s salary (stored in the salary column of the employee table) must be greater than or equal to the salary value required to obtain this benefit (stored in the salary_req column of the benefits table). Therefore, the join condition is exactly that: salary_req <= employee.salary .

This exercise brings together JOIN s and GROUP BY . We select the benefit name and the count of employees who receive a given benefit. Therefore, we must group the data by the benefit_name column.

For each benefit, find the number of employees that receive it. Show two columns: the benefit_name and the count (name that column employee_count ). Impose a condition to select only benefits that are not received by any employee ( employee_count = 0 ).

This exercise is analogical to the previous exercise.

To list all benefits – including the ones that no employee receives – we use the LEFT JOIN statement: benefits LEFT JOIN employee . It ensures that all rows from the left-hand side table (here, benefits ) are listed, no matter whether they match the join condition or not.

Next, to select only the employee_count values of zero, we must impose a condition on the COUNT(employee.id) aggregate function. To do so, we use the HAVING clause (introduced in exercise 10).

This article covered the basics of MySQL queries, including selecting and filtering data, ordering and grouping data, and joining multiple tables.

If you enjoyed the MySQL practice exercises showcased in this article, I suggest taking our SQL Practice Set in MySQL for more.

For additional help on SQL practice, check out these blog posts:

• SQL for Data Analysis: 15 Practical Exercises with Solutions .
• 10 Beginner SQL Practice Exercises with Solutions .
• How to Create Your Own Database to Practice SQL .
• 12 Ways to Practice SQL Online .

And remember, practice makes perfect.

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DBMS Tutorial – Learn Database Management System

Database Management System is a software or technology used to manage data from a database. Some popular databases are MySQL, Oracle, MongoDB, etc. DBMS provides many operations e.g. creating a database, Storing in the database, updating an existing database, delete from the database. DBMS is a system that enables you to store, modify and retrieve data in an organized way. It also provides security to the database.

In this Database Management System tutorial you’ll learn basic to advanced topics like ER model, Relational Model, Relation Algebra, Normalization, File Organization, etc.

Table of Content

Introduction :

Entity relationship model :, relational model :, relational algebra :, functional dependencies :, normalisation :, transactions and concurrency control :, indexing, b and b+ trees :, file organization:, advanced topics :, sql tutorial, advantages of dbms, disadvantages of dbms, quick links :.

• DBMS Introduction | Set 1
• DBMS Introduction | Set 2 (3-Tier Architecture)
• DBMS Architecture 2-level 3-level
• Need For DBMS
• Data Abstraction and Data Independence
• Database Objects
• Multimedia Database
• Categories of End Users
• Use of DBMS in System Software
• Choice of DBMS | Economic factors
• Enhanced ER Model
• Minimization of ER Diagram
• ER Model: Generalization, Specialization and Aggregation
• Recursive Relationships
• Impedance Mismatch
• Relational Model and CODD Rules
• Relational Model
• Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
• Number of possible Superkeys
• Anomalies in Relational Model
• Mapping from ER Model to Relational Model
• Strategies for Schema design
• Schema Integration
• Star Schema in Data Warehouse modeling
• Data Warehouse Modeling | Snowflake Schema
• Dimensional Data Modeling

>> Quiz on ER and Relational Model

• Introduction
• Basic Operators
• Extended Operators
• Inner Join vs Outer Join
• Join operation Vs nested query
• DBMS | Tupple Relational Calculus
• Row oriented vs. column oriented data stores
• How to solve Relational Algebra Problems for GATE
• How to Solve Relational Algebra Problems for GATE
• Functional Dependency and Attribute Closure
• Finding Attribute Closure and Candidate Keys using Functional Dependencies
• Armstrong’s Axioms in Functional Dependency
• Equivalence of Functional Dependencies
• Canonical Cover
• Normal Forms
• Minimum relations satisfying 1NF
• The Problem of redundancy in Database
• Dependency Preserving Decomposition
• Lossless Join Decomposition
• LossLess Join and Dependency Preserving Decomposition
• How to find the Highest Normal Form of a Relation
• Domain Key normal form
• Introduction of 4th and 5th Normal form
• Denormalization in Databases
• DBMS | Data Replication

>> Quiz on Normal Forms

• ACID Properties
• Concurrency Control -Introduction
• Implementation of Locking in DBMS
• Concurrency Control Protocols – Lock Based Protocol
• Concurrency Control Protocol | Graph Based Protocol
• Concurrency Control Protocol | Two Phase Locking (2-PL)-I
• Concurrency Control Protocol | Two Phase Locking (2-PL)-II
• Concurrency Control Protocol | Two Phase Locking (2-PL)-III
• Concurrency Control Protocol | Multiple Granularity Locking
• Concurrency Control Protocol | Thomas Write Rule
• Concurrency Control | Polygraph to check View Serializabilty
• DBMS | Log based recovery
• Timestamp Ordering Protocols
• Introduction to TimeStamp and Deadlock Prevention Schemes
• Dirty read in SQL
• Types of Schedules
• Conflict Serializability
• View Serializability
• How to test if two schedules are View Equal or not ?
• Recoverability of Schedules
• Precedence Graph for testing Conflict Serializabilty
• Transaction Isolation Levels in DBMS
• Database Recovery Techniques
• Starvation in DBMS
• Deadlock in DBMS
• DBMS | OLAP vs OLTP
• Types of OLAP Systems
• DBMS | Types of Recoverability of Schedules and easiest way to test schedule | Set 2
• Web Information Retrieval | Vector Space Model
• Why recovery is needed?

>> Quiz on Transactions and concurrency control

• Indexing and its Types
• B-Tree | Set 1 (Introduction)
• B-Tree | Set 2 (Insert)
• B-Tree | Set 3 (Delete)
• B+ Tree (Introduction)
• Bitmap Indexing
• Inverted Index
• Difference between Inverted Index and Forward Index
• SQL queries on clustered and non-clustered Indexes

>> Practice questions on B and B+ Trees >> Quizzes on Indexing, B and B+ Trees

• File Organization – Set 1
• File Organization – Set 2 (Hashing in DBMS)
• File Organization – Set 3
• File Organization – Set 4

>> Quiz on File structures

• Query Optimization
• How to store a password in database?
• Storage Area Networks
• Network attached storage
• Data Warehousing
• Data Warehouse Architecture
• Characteristics and Functions of Data warehouse
• Difficulties of Implementing Data Warehouses
• Data Mining
• Data Mining | KDD process
• Data Mining | Sources of Data that can be mined
• ODBMS – Definition and overview
• Architecture of HBase
• Apache HBase
• Architecture and Working of Hive
• Apache Hive
• Difference between Hive and HBase
• Difference between RDBMS and HBase
• Challenges of database security
• Federated database management system issues
• Distributed Database System
• Functions of Distributed Database System
• Semantic Heterogeneity
• Advantages of Distributed database
• Comparison – Centralized, Decentralized and Distributed Systems
• Characteristics of Biological Data (Genome Data Management)
• Data Management issues in Mobile database
• Future Works in Geographic Information System
• Difference between Structured, Semi-structured and Unstructured data
• SQL | Tutorials
• Quiz on SQL

DBMS practices questions :

• Database Management Systems | Set 1
• Database Management Systems | Set 2
• Database Management Systems | Set 3
• Database Management Systems | Set 4
• Database Management Systems | Set 5
• Database Management Systems | Set 6
• Database Management Systems | Set 7
• Database Management Systems | Set 8
• Database Management Systems | Set 9
• Database Management Systems | Set 10
• Database Management Systems | Set 11

There are some following reasons to learn DBMS:

• Organizing and management of data: DBMS helps in managing large amounts of data in an organized manner. It provides features like create, edit, delete, and read.
• Data Security: DBMS provides Security to the data from the unauthorized person.
• Improved decision-making: From stored data in the database we can generate graphs, reports, and many visualizations which helps in decision-making.
• Consistency: In a traditional database model all things are manual or inconsistent, but DBMS enables to automation of the operations by queries.
• Complexity: DBMS can be hard to design, implement, and manage, needing specialized knowledge.
• Cost: High setup costs, including hardware, software, and skilled personnel, can be expensive. Ongoing maintenance adds to the cost.
• Performance Overhead: DBMS might slow down simple tasks due to their extra features and general-purpose nature.
• Security Risks: Centralizing data can create security risks. If the system is hacked, all data could be compromised.
• Resource Intensive: DBMS need a lot of memory, storage, and processing power, which can be costly.
• Data Integrity Issues: Complex systems can lead to data integrity problems if not managed well.

Understanding Database Management Systems (DBMS) is essential for managing and organizing data effectively. This DBMS tutorial has introduced you to key concepts like database models, SQL queries, normalization, and data security. With this knowledge, you can design efficient databases, maintain data integrity, and improve performance.

Database Management System(DBMS) – FAQs

Q.1 what is database.

A database is a collection of organized data which can easily be created, updated, accessed, and managed. Records are kept maintained in tables or objects. A tuple (row) represents a single entry in a table. DBMS manipulates data from the database in the form of queries given by the user.

Q.2 What are different languages present in DBMS?

DDL (Data Definition Language) : These are the collection of commands which are required to define the database. E.g., CREATE, ALTER, RENAME, TRUNCATE, DROP, etc. DML (Data Manipulation Language) : These are the collection of commands which are required to manipulate the data stored in a database. E.g., SELECT, UPDATE, INSERT, DELETE, etc. DCL (Data Control Language) : These are the collection of commands which are dealt with the user permissions and controls of the database system. E.g, GRANT, and REVOKE. TCL (Transaction Control Language) : These are the collection of commands which are required to deal with the transaction of the database. E.g., COMMIT, ROLLBACK, and SAVEPOINT.

Q.3 What are the ACID properties in DBMS?

The full form of ACID is Atomicity, Consistency, Isolation, and Durability these are the properties of DBMS that ensure a safe and secure way of sharing data among multiple users. A  – Atomic: All changes to the data must be performed successfully or not at all. C  – Consistent: Data must be in a consistent state before and after the transaction. I  – Isolated: No other process can change the data while the transaction is going on. D  – Durable: The changes made by a transaction must persist.

Q.4 What are the Advantages of DBMS?

The followings are the few advantages of DBMS : Data Sharing:  Data from the same database can be shared by multiple users at the same time. Integrity:  It allows the data stored in an organized and refined manner. Data Independence:  It allows changing the data structure without changing the composition of executing programs. Data Security:  DBMS comes with the tools to make the storage and transfer of databases secure and reliable. Authentication and encryption are the tools used in DBMS for data security.

• Last Minutes Notes(LMNs) on DBMS
• Quizzes on DBMS
• Practice Problems on DBMS
• DBMS interview questions | Set 1
• DBMS interview questions | Set 2

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6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
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Assignment 1 -- SQL as a Data Manipulation Language

Due: by class time friday, february 20 2004, assignment 2 -- practice writing queries, due: by class time monday, march 1, 2004.

• ▼MySQL Exercises
• Introduction
• ▼DML and DDL
• Create Table statement
• Insert Into statement
• Update Table statement
• Alter Table statement
• ▼Exercises on HR Database
• Basic SELECT statement
• Restricting and Sorting Data
• MySQL Aggregate Functions
• ▼Exercises on Northwind Database
• Products Table

MySQL Exercises, Practice, Solution

What is mysql.

MySQL is the world's most widely used open-source relational database management system (RDBMS), enabling the cost-effective delivery of reliable, high-performance and scalable Web-based and embedded database applications. It is widely-used as the database component of LAMP (Linux, Apache, MySQL, Perl/PHP/Python) web application software stack.

The best way we learn anything is by practice and exercise questions. We have started this section for those (beginner to intermediate) who are familiar with SQL and MySQL . Hope, these exercises help you to improve your MySQL query skills. Currently following sections are available, we are working hard to add more exercises. Happy Coding!

Exercises on Data Manipulation Language (DML) & Data Definition Language (DDL)

• MySQL Create Table statement [20 Exercises]
• MySQL Insert Into statement [14 Exercises]
• MySQL Update Table statement [9 Exercises]
• MySQL Alter Table statement [15 Exercises]

Exercises on HR Database

• MySQL Basic SELECT statement [19 Exercises]
• MySQL Restricting and Sorting Data [11 Exercises with Solutions]
• MySQL Aggregate Functions and Group by [14 Exercises with Solutions]
• MySQL Subqueries [22 Exercises with Solution ]
• MySQL JOINS [13 Exercises with Solution]
• MySQL Date Time [21 Exercises with Solution]
• MySQL String Functions [17 Exercises with Solution]

Exercises on Northwind Database

• Exercises on Products Table [ 10 Exercises]

More to Come !

Structure of 'hr' database :

You may download the structure and data of the database used here

Click here to get Oracle Hr Database

Structure of 'northwind' database:

Click here to get Northwind sample databases for Microsoft SQL Server.

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Database Management System Assignment: DBMS Fundamentals

Task: Students are required to analyse the weekly lecture material of weeks 1 to 11 by creating concise summaries of the theoretical concepts contained in the course lecture slides and providing their own reflections over it.

Introduction and Background to the theme of database management system assignment: Week-1 Theory, Concepts, and Outcomes: Important Topics Covered in this database management system assignment: 1. Data and Information comparison 2. Database types, design and their importance in decision making 3. Difference between Database and file system 4. Function of DBMS 5. Role of open source database systems 6. Data Governance and Data Quality

Definitions: Data Quality and Governance: Herein database management system assignment, data quality is meant by the accuracy, relevancy, uniqueness, integrity, security, accessibility, and reliability of data, which free from any anomalies and redundancies, Data Governance is the strategies and policies defined by an organization in order to ensure data quality. Database: Database is an integrated structure that stores user metadata in the form of a table containing attributes and tuples to store data in a systematic way (structure) to simplify user access to the data. DBMS: As per the research on database management system assignment, the database management system is the collection of programs that define, manipulate, abstract, retrieves, and efficiently manages data in a database and provide an understandable version of havoc data to the application user.

Reflection: For instance, if we take an example of an e-commerce system selling products from multiple 3rd party merchants to the end-user customers, a DBMS will be required to store, record, maintain (update), eliminate inconsistency, enable the user to quickly and accurately access data by retrieving the user logical data from the client end, and processing the query request (physical data format) from the server end and quickly sending the results back to the client end-user.

Outcome: Throughout this database management system assignment, we have learned the importance of Database design, data abstraction, anomalies, redundancies, the importance of the use of DBMS over traditional file systems for storing and using data. How poorly designed databases can give rise to confusion leading to catastrophic consequences to an organization. Week-2 Theory, Concepts, and Outcomes:

Important Topics Covered within this database management system assignment: 1. Data Models evolution, classification and basic building blocks (components) 2. Business rules and their influence in database design

Definitions: Data Models: As discussed in the database management system assignment, it represents the logical structure of a database consisting of the entity, attribute, relationship, and constraint into a real-world model by simplifying the complex data structures into an easier format. It defines how data are stored and processed inside the Database system and act as fundamental entities that enforce data abstraction in DBMS.

Basic building blocks: entity attributes relationship and constrains Data Model evolution &classification: Hierarchical, Network, Relational, Entity Relationship, and Object-Oriented Business Rules: The database management system assignmentexamines that it is a set of precise and unambiguous descriptions of policies, procedures, or principles within a specific organization that is recorded and updated for maintaining the data quality and data governance in the organization.

Reflection: The Ecommerce system that we discussed earlier needs to be compared with the real-world scenario to simplify it, in terms of both the users and the programmers, which can be achieved with the help of data models and robust business rules of the company. Data models explored in the database management system assignment hide the non-important data through different level (degrees) of data abstraction among users andprogrammers to simplify the database system and make it consistent while performing similar and repetitive operations to produce detailed query results,

Outcome obtained in the database management system assignment: The importance of data models and business rules in an organization with the evolution of the data models, such as hierarchical model which was traditional data model with a lot of limitations is replaced by the network, relational and entity relational models which introduced the concept of schema, attributes, SQL, ERD’s which are still used to handle huge user data and reduce data inconsistency, but when the user data becomes enormous and even more complex to record and access, the final data model called the Object-oriented data (semantic data) model is introduced which is the basic foundation of modern programming, processing, and representation of data.

Week-3 Theory, Concepts, and Outcomes: What are the important topics covered within this database management system assignment? 1. Relational Database Model- basic components, sub-components, how redundancy is handled and importance of indexing 2. Relations (Tables in DBMS) and its constituents 3. Relational database operators, data dictionary, and system catalogue

Definitions: Table or Relations: As noted herein database management system assignment, it is the standard data structure consisting of attributes (columns) and tuples (rows) that logically represent data and records in a 2D structure and also forms as a medium to connect other relations in the database. The set of attributes (Keys) inside a relation plays a major role in identifying, accessing, processing data and information within the database environment.

Keys: Keys are the set of attributes that are the fundamental constituents that give meaning to the relational schema. Different type of keys mentioned in the context of database management system assignment such as primary key, composite key, super key, candidate key, foreign key, and secondary key serves different functions and defines the relational database while making the database design consistent.

Data Dictionary:A data dictionary or system catalogdefines different objects within a database, it helps the programmer or user to access all the relational database relationships in a hierarchical manner.

Reflections: In terms of the E-commerce system that we have considered, given the enormous data that needs to be stored, processed, and utilized in a systematic manner, an object-oriented approach is must to reduce data redundancy in relations and using the keys properly for the right use of the database system. For instance, the customer records will be present in multiple relations such as registration information, order information, delivery information; the idea is to efficiently store and access the same data without creating inconsistencies.

Outcome obtained in the database management system assignment: A good database design is the most important aspect in DBMS, which forms its foundation and depends upon comprehension of relevant entities, attributes, and relationships among entities. It is mentioned in this database management system assignment that this can be achieved by representing the entire database through the Entity-Relationship Diagrams (ERD).

Week-5 Theory, Concepts, and Outcomes: Important Topics Covered within this database management system assignment: 1. Entity Relationship Components 2. Definition of Entity and its roll in database design 3. ERD components role in the implementation of database design 4. Compromises that the DB designers have to make due to the requirement of reconciliation of conflicting goals in the database design

Definitions: Attributes:Attributes are the characteristics of entities that are generally atomic in nature (cannot be further divided into subparts), for instance, Customer is an entity to which customer_id, name, gender, contact information, address, etc are attributes. Attributes type ranges from the composite attribute, simple attribute, single-value attribute, multi-valued attributes, and their characteristics define their importance in Entity relationships.

Relationships: It forms the association between different entities by linking disparate data items in the context of databases, relationships exist among two relational database tables and ideally operates in both directions, ie: 1:1. 1:M, M:1, M: M. Its classification is difficult to establish as only one side of the relationship is known.A relationship is established through connectivity and cardinality along with the business rules. Relationship lifetime is dependent on the existence of entities. The strength of a relationship is defined by the Primary Key’s existence in the parent key entity.

Reflections: As per the scenario of database management system assignment, the eCommerce platform depends upon the attributes and relationships for the entire thing to work flawlessly. For instance, the relationship between several tables such as products, customers_info, customer_order info, delivery_details, inventory_details (from where the products purchase data will be updated) are formed and maintained through the integrity of the entities and their respected relationships with different tables.

Outcome: The objective of this chapter of database management system assignment is to reveal the importance of attributes, entities, relationships,relationship strengths, degrees, participation, dependencies, etc in databases. The above concepts apparently define the complexities of the database management system and its efficiency in strengthening the organization data with correct utilization of these concepts while database design and maintenance.

Week-6 Theory, Concepts, and Outcomes: Important Topics Covered: 1. Extended Entity Relationship 2. Use of Entity Clusters 3. Selection of Primary Keys 4. Special Data Modelling Cases 5. Developing Data Models based on EER diagrams

Definitions: Extended Entity-Relationship Model:EERM is a conceptual data model that integrates the extensions to the original ER model in the form of semantic constructs that were used in database design. To represent an EERM, an EER diagram (EERD) is used. Inheritance:It is an important feature of Generalization (1:M) and Specialization(M:1) entity-relationship. It allows lower-level entities to inherit the attributes of higher-level entities. This concept is derived from the basic object-oriented approach to programming. It enables entity subtype to inherit attributes and relationships of supertype (entity type that is related to single or multiple entity types having similar traits. At the implementation level, supertype) and its subtype(s) maintain a 1:1 relationship.

Entity Integrity: The integrity of the entity dependson the choice of primary keys and determines the efficiency and effectiveness of the Database, The primary key and foreign key collaboratively functions to implement relationships.

Reflection: As per the scenario the e-commerce platform must be built upon the solid foundation of a flexible database design, thorough knowledge of data modeling cases is a requisite for the designer and developer of the system. All the special cases should be taken into account before implementation as huge systems such as E-commerce platforms requires a lot of data processing, and backup of an efficient database without which it may never return relevant results to customer queries with an increase in data anomalies thereby making the system inconsistent.

Outcome: The objective of this chapter is to comprehend the complex data models with the help of specialization and generalization of attributes, use of relevant primary keys, and entity types, forming hierarchical relationships with the use of these keys to foster the special data modeling cases.

Week-7 Theory, Concepts, and Outcomes: Important Topics Covered within this database management system assignment: 1. Introduction to Normalization and its role in Database design 2. Use of Normalization, ER modeling concurrently to enhance the database design 3. Use of denormalization in special cases to efficiently generate information

Definitions: Normalization: Normalization is the process of inspecting and rectifying drawbacks in table structures to reduce data redundancies by minimizing data anomalies. It is enabled through dividing it into five forms, 1NF, 2NF, 3NF, 4NF, and BCNF. The process is defined as each table representing a single subject, the same data is eliminated from multiple tables and only one copy is stored in a single table, all attributes depends on the primary key, each table void of insertion, update, deletion anomalies (Vathy-Fogarassy and Hugyák, 2017).

Reflection: For huge databases to maintain enormous records of an Ecommerce business, the tables designed should be normalized to all levels of normalization in order to remove all kinds of data anomalies and inconsistencies. By evaluating the primary key assignments, naming conventions, derived attributes, refining attribute atomicity, new attributes and relationships identification, and maintaining historical accuracy the overall database structure can be enhanced for increased efficiency and consistency of the entire system.

Outcome: The objective noted herein database management system assignment was to introduce an important concept called normalization in database systems to further improve the database designs by constantly evaluating the relationships and normalize them through different levels of normalization to make them free from anomalies that continue the process till all the remaining anomalies are eliminated from the database.

Week-8 Theory, Concepts, and Outcomes: Important Topics Covered in the present context of database management system assignment: 1. Introduction to Structured Query Language 2. Use of SQL by DBA’s 3. Role of SQL in data manipulation 4. Trigger an SQL query to retrieve relevant information

Definitions: SQL: Structured query language is a domain-specific language used in programming and supports most of the programming languagesto define, manipulate and use data in the relational database management system (RDBMS), it is further classified into two basic categories:

DDL (Data Definition Language) and DML (Data Manipulation Language). The best part of SQL is it easy to implement due to the simple syntax and it uses basic commands to operate and categorized as a non-procedural language (Wylotet al. 2018).

Database Schema: It is the basic structure of the entire database that depicts the logical view of the database. It formulates the organization of data and association of relations among them. It defines all the constraints that need to be applied to the data. Authentication is required in order to access the database, which is controlled by the DBA, who assigns registered users and validates their id, each time they request access.

Reflection: As per the given scenario SQL act as the medium for the Ecommerce website that enables the communication between the client end-user interface application, server end programming language and the Database that receives the request sent by the front end user through the application software via the server end layer, which triggers relevant functions to access certain information and process it with the available resources and send it back to the client end. From ordering a certain product to making the final payments, every event trigger specific functions in the DBMS to record and process data accordingly to fulfill the request.

Outcome: The objective of this chapter is to define the power of the introduction of a non-procedural language in DBMS to handle complex queries efficiently and with minimum processing time. SQL is not only easy to apply in a different programming language but also a powerful database tool for efficient data handling in complex RDBMS through different SQLcommands.

Week-9 Theory, Concepts, and Outcomes: Important Topics Covered: 1. Use of database design to synergize with the information system. 2. To follow the SDLC framework for implementing successful information systems 3. Database Life Cycle (DBLC) 4. Conducting evaluation and revision in SDLC and DBLC frameworks 5. Role of DBA

Definitions: SDLC: System Development Life Cycle model is a framework used in information systems for efficient management of the system following an object-oriented approach towards creation, development, and maintenance of the database (Wu, Sakr and Zhu, 2017). It is useful for an organization that wants to implement a complex database with huge data and records to use SDLC for designing the database. SDLC has five phases, planning, analysis, detailed systems design, implementation, and maintenance. It is an iterative approach towards the development and maintenance of the DBMS that is repeated throughout the entire DBLC.

DBLC: The Database Life Cycle has six phases, database initial study, database design, implementation and loading, testing and evaluation, operation, and maintenance and evolution. It goes in order with SDLC.

Reflection: For large real-life applications like e-commerce websites where multiple users log on at a time and access data from it, very complex systems are running behind the UI's to serve the purpose while optimizing the results of the end-user requests with efficient DBMS working behind the servers powered by efficient frameworks such as SDLC that uses the Object-oriented concepts to maintain the database system.

Outcome: The objective of the database management system assignmentis to learn about the SDLC framework in DBMS, by breaking the problems and constraints of an organization with different views (business and designer view), identifying the problems, objectives, scopes and limitations of the database, before planning the design phase (keeping the basic design overviews in mind, such as logical and physical design), database security measures, testing and evaluation of the DBMS, operation, maintenance and evolution phase to further enhance it. The Role of DBA in implementing and enforcing company standards and administrating utilities and applications.

References: Vathy-Fogarassy, Á. and Hugyák, T., 2017. Uniform data access platform for SQL and NoSQL database systems. Information Systems, 69, pp.93-105.

Wu, D., Sakr, S. and Zhu, L., 2017.Big data storage and data models.In Handbook of big data technologies (pp. 3-29).Database management system assignmentSpringer, Cham.

Wylot, M., Hauswirth, M., Cudré-Mauroux, P. and Sakr, S., 2018. RDF data storage and query processing schemes: A survey. ACM Computing Surveys (CSUR), 51(4), pp.1-36.

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Module 12: Microsoft Access Basic Skills

Assignment: create new access database.

In this assignment, you’ll create a new database and set it up for data to be entered. This year you decided to keep new business cards (physical and electronic) you receive organized with by creating an Access database to store all their information for quick access.

To complete this assignment, follow the directions then submit your assignment. If you get stuck on a step, review this module and ask your classmates for help in the discussion forum.

• Save File: Save all of your work again.
• Submit the database file in your course online.

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• Assignment: Create New Access Database. Authored by : Sherri Pendleton. Provided by : Lumen Learning. License : CC BY: Attribution

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CREATE DATABASE

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Creates a new database.

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* SQL Server *  

SQL Database

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In SQL Server, this statement creates a new database and the files used and their filegroups. It can also be used to create a database snapshot, or attach database files to create a database from the detached files of another database.

Create a database.

For more information about the syntax conventions, see Transact-SQL syntax conventions .

Attach a database

Create a database snapshot

database_name

This is the name of the new database. Database names must be unique within an instance of SQL Server and comply with the rules for identifiers .

database_name can be a maximum of 128 characters, unless a logical name is not specified for the log file. If a logical log file name is not specified, SQL Server generates the logical_file_name and the os_file_name for the log by appending a suffix to database_name . This limits database_name to 123 characters so that the generated logical file name is no more than 128 characters.

If data file name is not specified, SQL Server uses database_name as both the logical_file_name and as the os_file_name . The default path is obtained from the registry. The default path can be changed in the Server Properties (Database Settings Page) in Management Studio. Changing the default path requires restarting SQL Server.

CONTAINMENT = { NONE | PARTIAL }

Applies to : SQL Server 2012 (11.x) and later

Specifies the containment status of the database. NONE = non-contained database. PARTIAL = partially contained database.

Specifies that the disk files used to store the data sections of the database, data files, are explicitly defined. ON is required when followed by a comma-separated list of <filespec> items that define the data files for the primary filegroup. The list of files in the primary filegroup can be followed by an optional, comma-separated list of <filegroup> items that define user filegroups and their files.

Specifies that the associated <filespec> list defines the primary file. The first file specified in the <filespec> entry in the primary filegroup becomes the primary file. A database can have only one primary file. For more information, see Database Files and Filegroups .

If PRIMARY is not specified, the first file listed in the CREATE DATABASE statement becomes the primary file.

Specifies that the disk files used to store the database log, log files, are explicitly defined. LOG ON is followed by a comma-separated list of <filespec> items that define the log files. If LOG ON is not specified, one log file is automatically created, which has a size that is 25 percent of the sum of the sizes of all the data files for the database, or 512 KB, whichever is larger. This file is placed in the default log-file location. For information about this location, see View or Change the Default Locations for Data and Log Files in SSMS .

LOG ON cannot be specified on a database snapshot.

COLLATE collation_name

Specifies the default collation for the database. Collation name can be either a Windows collation name or a SQL collation name. If not specified, the database is assigned the default collation of the instance of SQL Server. A collation name cannot be specified on a database snapshot.

A collation name cannot be specified with the FOR ATTACH or FOR ATTACH_REBUILD_LOG clauses. For information about how to change the collation of an attached database, visit this Microsoft Web site .

For more information about the Windows and SQL collation names, see COLLATE .

Contained databases are collated differently than non-contained databases. For more information, see Contained Database Collations .

WITH <option>

<filestream_option>.

NON_TRANSACTED_ACCESS = { OFF | READ_ONLY | FULL }

Applies to : SQL Server 2012 (11.x) and later.

Specifies the level of non-transactional FILESTREAM access to the database.

DIRECTORY_NAME = <directory_name>

A windows-compatible directory name. This name should be unique among all the Database_Directory names in the SQL Server instance. Uniqueness comparison is case-insensitive, regardless of SQL Server collation settings. This option should be set before you create a FileTable in this database.

The following options are allowable only when CONTAINMENT has been set to PARTIAL. If CONTAINMENT is set to NONE, errors will occur.

DEFAULT_FULLTEXT_LANGUAGE = <lcid> | <language name> | <language alias>

See Configure the default full-text language Server Configuration Option for a full description of this option.

DEFAULT_LANGUAGE = <lcid> | <language name> | <language alias>

See Configure the default language Server Configuration Option for a full description of this option.

NESTED_TRIGGERS = { OFF | ON }

See Configure the nested triggers Server Configuration Option for a full description of this option.

TRANSFORM_NOISE_WORDS = { OFF | ON }

See transform noise words Server Configuration Option for a full description of this option.

TWO_DIGIT_YEAR_CUTOFF = { 2049 | <any year between 1753 and 9999> }

Four digits representing a year. 2049 is the default value. See Configure the two digit year cutoff Server Configuration Option for a full description of this option.

DB_CHAINING { OFF | ON }

When ON is specified, the database can be the source or target of a cross-database ownership chain.

When OFF, the database cannot participate in cross-database ownership chaining. The default is OFF.

The instance of SQL Server will recognize this setting when the cross db ownership chaining server option is 0 (OFF). When cross db ownership chaining is 1 (ON), all user databases can participate in cross-database ownership chains, regardless of the value of this option. This option is set by using sp_configure .

To set this option, requires membership in the sysadmin fixed server role. The DB_CHAINING option cannot be set on these system databases: master , model , tempdb .

TRUSTWORTHY { OFF | ON }

When ON is specified, database modules (for example, views, user-defined functions, or stored procedures) that use an impersonation context can access resources outside the database.

When OFF, database modules in an impersonation context cannot access resources outside the database. The default is OFF.

TRUSTWORTHY is set to OFF whenever the database is attached.

By default, all system databases except the msdb database have TRUSTWORTHY set to OFF. The value cannot be changed for the model and tempdb databases. We recommend that you never set the TRUSTWORTHY option to ON for the master database.

PERSISTENT_LOG_BUFFER=ON ( DIRECTORY_NAME='' )

When this option is specified, the transaction log buffer is created on a volume that is located on a disk device backed by Storage Class Memory (NVDIMM-N nonvolatile storage), also known as a persistent log buffer. For more information, see Transaction Commit latency acceleration using Storage Class Memory . Applies to : SQL Server 2017 (14.x) and newer.

LEDGER = {ON | OFF }

When set to ON , it creates a ledger database, in which the integrity of all user data is protected. Only ledger tables can be created in a ledger database. The default is OFF . The value of the LEDGER option cannot be changed once the database is created. For more information, see Configure a ledger database .

CREATE DATABASE ... FOR ATTACH [ WITH < attach_database_option > ]

Specifies that the database is created by attaching an existing set of operating system files. There must be a <filespec> entry that specifies the primary file. The only other <filespec> entries required are those for any files that have a different path from when the database was first created or last attached. A <filespec> entry must be specified for these files.

FOR ATTACH requires the following:

• All data files (MDF and NDF) must be available.
• If multiple log files exist, they must all be available.

If a read/write database has a single log file that is currently unavailable, and if the database was shut down with no users or open transactions before the attach operation, FOR ATTACH automatically rebuilds the log file and updates the primary file. In contrast, for a read-only database, the log cannot be rebuilt because the primary file cannot be updated. Therefore, when you attach a read-only database with a log that is unavailable, you must provide the log files, or the files in the FOR ATTACH clause.

A database created by a more recent version of SQL Server cannot be attached in earlier versions.

In SQL Server, any full-text files that are part of the database that is being attached will be attached with the database. To specify a new path of the full-text catalog, specify the new location without the full-text operating system file name. For more information, see the Examples section.

Attaching a database that contains a FILESTREAM option of "Directory name", into a SQL Server instance will prompt SQL Server to verify that the Database_Directory name is unique. If it is not, the attach operation fails with the error, FILESTREAM Database_Directory name is not unique in this SQL Server instance . To avoid this error, the optional parameter, directory_name , should be passed in to this operation.

FOR ATTACH cannot be specified on a database snapshot.

FOR ATTACH can specify the RESTRICTED_USER option. RESTRICTED_USER allows for only members of the db_owner fixed database role and dbcreator and sysadmin fixed server roles to connect to the database, but does not limit their number. Attempts by unqualified users are refused.

<service_broker_option>

If the database uses Service Broker, use the WITH <service_broker_option> in your FOR ATTACH clause:

Controls Service Broker message delivery and the Service Broker identifier for the database. Service Broker options can only be specified when the FOR ATTACH clause is used.

ENABLE_BROKER

Specifies that Service Broker is enabled for the specified database. That is, message delivery is started, and is_broker_enabled is set to true in the sys.databases catalog view. The database retains the existing Service Broker identifier.

Creates a new service_broker_guid value in both sys.databases and the restored database. Ends all conversation endpoints with cleanup. The broker is enabled, but no message is sent to the remote conversation endpoints. Any route that references the old Service Broker identifier must be re-created with the new identifier.

ERROR_BROKER_CONVERSATIONS

Ends all conversations with an error stating that the database is attached or restored. The broker is disabled until this operation is completed and then enabled. The database retains the existing Service Broker identifier.

When you attach a replicated database that was copied instead of being detached, consider the following:

• If you attach the database to the same server instance and version as the original database, no additional steps are required.
• If you attach the database to the same server instance but with an upgraded version, you must execute sp_vupgrade_replication to upgrade replication after the attach operation is complete.
• If you attach the database to a different server instance, regardless of version, you must execute sp_removedbreplication to remove replication after the attach operation is complete.

Attach works with the vardecimal storage format, but the SQL Server Database Engine must be upgraded to at least SQL Server 2005 (9.x) SP2. You cannot attach a database using vardecimal storage format to an earlier version of SQL Server. For more information about the vardecimal storage format, see Data Compression .

When a database is first attached or restored to a new instance of SQL Server, a copy of the database master key (encrypted by the service master key) is not yet stored in the server. You must use the OPEN MASTER KEY statement to decrypt the database master key (DMK). Once the DMK has been decrypted, you have the option of enabling automatic decryption in the future by using the ALTER MASTER KEY REGENERATE statement to provision the server with a copy of the DMK, encrypted with the service master key (SMK). When a database has been upgraded from an earlier version, the DMK should be regenerated to use the newer AES algorithm. For more information about regenerating the DMK, see ALTER MASTER KEY . The time required to regenerate the DMK key to upgrade to AES depends upon the number of objects protected by the DMK. Regenerating the DMK key to upgrade to AES is only necessary once, and has no impact on future regenerations as part of a key rotation strategy. For information about how to upgrade a database by using attach, see Upgrade a Database Using Detach and Attach .

We recommend that you do not attach databases from unknown or untrusted sources. Such databases could contain malicious code that might execute unintended Transact-SQL code or cause errors by modifying the schema or the physical database structure. Before you use a database from an unknown or untrusted source, run DBCC CHECKDB on the database on a nonproduction server, and also examine the code, such as stored procedures or other user-defined code, in the database.

The TRUSTWORTHY and DB_CHAINING options have no effect when attaching a database.

FOR ATTACH_REBUILD_LOG

Specifies that the database is created by attaching an existing set of operating system files. This option is limited to read/write databases. There must be a <filespec> entry specifying the primary file. If one or more transaction log files are missing, the log file is rebuilt. The ATTACH_REBUILD_LOG automatically creates a new, 1-MB log file. This file is placed in the default log-file location. For information about this location, see View or Change the Default Locations for Data and Log Files in SSMS .

If the log files are available, the Database Engine uses those files instead of rebuilding the log files.

FOR ATTACH_REBUILD_LOG requires the following conditions:

• A clean shutdown of the database.

This operation breaks the log backup chain. We recommend that a full database backup be performed after the operation is completed. For more information, see BACKUP .

Typically, FOR ATTACH_REBUILD_LOG is used when you copy a read/write database with a large log to another server where the copy will be used mostly, or only, for read operations, and therefore requires less log space than the original database.

FOR ATTACH_REBUILD_LOG cannot be specified on a database snapshot.

For more information about attaching and detaching databases, see Database Detach and Attach .

<filespec>

Controls the file properties.

NAME logical_file_name

Specifies the logical name for the file. NAME is required when FILENAME is specified, except when specifying one of the FOR ATTACH clauses. A FILESTREAM filegroup cannot be named PRIMARY.

logical_file_name

Is the logical name used in SQL Server when referencing the file. Logical_file_name must be unique in the database and comply with the rules for identifiers . The name can be a character or Unicode constant, or a regular or delimited identifier.

FILENAME { ' os_file_name ' | ' filestream_path ' }

Specifies the operating system (physical) file name.

' os_file_name '

Is the path and file name used by the operating system when you create the file. The file must reside on one of the following devices: the local server on which SQL Server is installed, a Storage Area Network [SAN], or an iSCSI-based network. The specified path must exist before executing the CREATE DATABASE statement. For more information, see Database Files and Filegroups later in this article.

SIZE, MAXSIZE, and FILEGROWTH parameters can be set when a UNC path is specified for the file.

If the file is on a raw partition, os_file_name must specify only the drive letter of an existing raw partition. Only one data file can be created on each raw partition.

Raw partitions are not supported in SQL Server 2014 and later versions.

Data files should not be put on compressed file systems unless the files are read-only secondary files, or the database is read-only. Log files should never be put on compressed file systems.

' filestream_path '

For a FILESTREAM filegroup, FILENAME refers to a path where FILESTREAM data will be stored. The path up to the last folder must exist, and the last folder must not exist. For example, if you specify the path C:\MyFiles\MyFilestreamData , C:\MyFiles must exist before you run ALTER DATABASE, but the MyFilestreamData folder must not exist.

The filegroup and file ( <filespec> ) must be created in the same statement.

The SIZE and FILEGROWTH properties do not apply to a FILESTREAM filegroup.

Specifies the size of the file.

SIZE cannot be specified when the os_file_name is specified as a UNC path. SIZE does not apply to a FILESTREAM filegroup.

Is the initial size of the file.

When size is not supplied for the primary file, the Database Engine uses the size of the primary file in the model database. The default size of the model database is 8 MB (beginning with SQL Server 2016 (13.x)) or 1 MB (for earlier versions). When a secondary data file or log file is specified, but size is not specified for the file, the Database Engine makes the file 8 MB (beginning with SQL Server 2016 (13.x)) or 1 MB (for earlier versions). The size specified for the primary file must be at least as large as the primary file of the model database.

The kilobyte (KB), megabyte (MB), gigabyte (GB), or terabyte (TB) suffixes can be used. The default is MB. Specify a whole number. Do not include a decimal. Size is an integer value. For values greater than 2147483647, use larger units.

MAXSIZE max_size

Specifies the maximum size to which the file can grow. MAXSIZE cannot be specified when the os_file_name is specified as a UNC path.

Is the maximum file size. The KB, MB, GB, and TB suffixes can be used. The default is MB. Specify a whole number. Do not include a decimal. If max_size is not specified, the file grows until the disk is full. Max_size is an integer value. For values greater than 2147483647, use larger units.

Specifies that the file grows until the disk is full. In SQL Server, a log file specified with unlimited growth has a maximum size of 2 TB, and a data file has a maximum size of 16 TB.

There is no maximum size when this option is specified for a FILESTREAM container. It continues to grow until the disk is full.

FILEGROWTH growth_increment

Specifies the automatic growth increment of the file. The FILEGROWTH setting for a file cannot exceed the MAXSIZE setting. FILEGROWTH cannot be specified when the os_file_name is specified as a UNC path. FILEGROWTH does not apply to a FILESTREAM filegroup.

growth_increment

Is the amount of space added to the file every time that new space is required.

The value can be specified in MB, KB, GB, TB, or percent (%). If a number is specified without an MB, KB, or % suffix, the default is MB. When % is specified, the growth increment size is the specified percentage of the size of the file at the time the increment occurs. The size specified is rounded to the nearest 64 KB, and the minimum value is 64 KB.

A value of 0 indicates that automatic growth is off and no additional space is allowed.

If FILEGROWTH is not specified, the default values are:

<filegroup>

Controls the filegroup properties. Filegroup cannot be specified on a database snapshot.

FILEGROUP filegroup_name

Is the logical name of the filegroup.

filegroup_name

filegroup_name must be unique in the database and cannot be the system-provided names PRIMARY and PRIMARY_LOG. The name can be a character or Unicode constant, or a regular or delimited identifier. The name must comply with the rules for identifiers .

CONTAINS FILESTREAM

Specifies that the filegroup stores FILESTREAM binary large objects (BLOBs) in the file system.

Specifies the named filegroup is the default filegroup in the database.

CONTAINS MEMORY_OPTIMIZED_DATA

Applies to : SQL Server 2014 (12.x) and later

Specifies that the filegroup stores memory_optimized data in the file system. For more information, see In-Memory Optimization Overview and Usage Scenarios . Only one MEMORY_OPTIMIZED_DATA filegroup is allowed per database. For code samples that create a filegroup to store memory-optimized data, see Creating a Memory-Optimized Table and a Natively Compiled Stored Procedure .

database_snapshot_name

Is the name of the new database snapshot. Database snapshot names must be unique within an instance of SQL Server and comply with the rules for identifiers. database_snapshot_name can be a maximum of 128 characters.

ON ( NAME = logical_file_name , FILENAME =' os_file_name ') [ ,... n ]

For creating a database snapshot, specifies a list of files in the source database. For the snapshot to work, all the data files must be specified individually. However, log files are not allowed for database snapshots. FILESTREAM filegroups are not supported by database snapshots. If a FILESTREAM data file is included in a CREATE DATABASE ON clause, the statement will fail and an error will be raised.

For descriptions of NAME and FILENAME and their values, see the descriptions of the equivalent <filespec> values.

When you create a database snapshot, the other <filespec> options and the keyword PRIMARY are disallowed.

AS SNAPSHOT OF source_database_name

Specifies that the database being created is a database snapshot of the source database specified by source_database_name . The snapshot and source database must be on the same instance.

Prior to SQL Server 2019, the source database for a database snapshot could not contain a MEMORY_OPTIMIZED_DATA filegroup. Support for in-memory database snapshots was added in SQL Server 2019.

For more information, see Database Snapshots .

The master database should be backed up whenever a user database is created, modified, or dropped.

The CREATE DATABASE statement must run in autocommit mode (the default transaction management mode) and is not allowed in an explicit or implicit transaction.

You can use one CREATE DATABASE statement to create a database and the files that store the database. SQL Server implements the CREATE DATABASE statement by using the following steps:

• The SQL Server uses a copy of the model database to initialize the database and its metadata.
• A service broker GUID is assigned to the database.
• The Database Engine then fills the rest of the database with empty pages, except for pages that have internal data that records how the space is used in the database.

A maximum of 32,767 databases can be specified on an instance of SQL Server.

Each database has an owner that can perform special activities in the database. The owner is the user that creates the database. The database owner can be changed by using ALTER AUTHORIZATION .

Some database features depend on features or capabilities present in the file system for full functionality of a database. Some examples of features that depend on file system feature set include:

• DBCC CHECKDB
• Online backups using VSS and file snapshots
• Database snapshot creation
• Memory Optimized Data filegroup

Database Files and Filegroups

Every database has at least two files, a primary file and a transaction log file , and at least one filegroup. A maximum of 32,767 files and 32,767 filegroups can be specified for each database.

When you create a database, make the data files as large as possible based on the maximum amount of data you expect in the database.

We recommend that you use a Storage Area Network (SAN), iSCSI-based network, or locally attached disk for the storage of your SQL Server database files, because this configuration optimizes SQL Server performance and reliability.

• Database Snapshots

You can use the CREATE DATABASE statement to create a read-only, static view, a database snapshot of the source database . A database snapshot is transactionally consistent with the source database as it existed at the time when the snapshot was created. A source database can have multiple snapshots.

When you create a database snapshot, the CREATE DATABASE statement cannot reference log files, offline files, restoring files, and defunct files.

If creating a database snapshot fails, the snapshot becomes suspect and must be deleted. For more information, see DROP DATABASE .

Each snapshot persists until it is deleted by using DROP DATABASE .

For more information, see Database Snapshots and Create a database snapshot (Transact-SQL) .

Database options

Several database options are automatically set whenever you create a database. For a list of these options, see ALTER DATABASE SET Options .

The model database and creating new databases

All user-defined objects in the model database are copied to all newly created databases. You can add any objects, such as tables, views, stored procedures, data types, and so on, to the model database to be included in all newly created databases.

When a CREATE DATABASE <database_name> statement is specified without additional size parameters, the primary data file is made the same size as the primary file in the model database.

Unless FOR ATTACH is specified, each new database inherits the database option settings from the model database. For example, the database option auto shrink is set to true in model and in any new databases you create. If you change the options in the model database, these new option settings are used in any new databases you create. Changing operations in the model database does not affect existing databases. If FOR ATTACH is specified on the CREATE DATABASE statement, the new database inherits the database option settings of the original database.

View database information

You can use catalog views, system functions, and system stored procedures to return information about databases, files, and filegroups. For more information, see System Views .

Permissions

Requires CREATE DATABASE , CREATE ANY DATABASE , or ALTER ANY DATABASE permission.

To maintain control over disk use on an instance of SQL Server, permission to create databases is typically limited to a few login accounts.

The following example provides the permission to create a database to the database user Fay.

Permissions on Data and Log Files

In SQL Server, certain permissions are set on the data and log files of each database. The following permissions are set whenever the following operations are applied to a database:

• Modified to add a new file

The permissions prevent the files from being accidentally tampered with if they reside in a directory that has open permissions.

Microsoft SQL Server 2005 Express edition does not set data and log file permissions.

A. Create a database without specifying files

The following example creates the database mytest and creates a corresponding primary and transaction log file. Because the statement has no <filespec> items, the primary database file is the size of the model database primary file. The transaction log is set to the larger of these values: 512 KB or 25% the size of the primary data file. Because MAXSIZE is not specified, the files can grow to fill all available disk space. This example also demonstrates how to drop the database named mytest if it exists, before creating the mytest database.

B. Create a database that specifies the data and transaction log files

The following example creates the database Sales . Because the keyword PRIMARY is not used, the first file ( Sales_dat ) becomes the primary file. Because neither MB nor KB is specified in the SIZE parameter for the Sales_dat file, it uses MB and is allocated in megabytes. The Sales_log file is allocated in megabytes because the MB suffix is explicitly stated in the SIZE parameter.

C. Create a database by specifying multiple data and transaction log files

The following example creates the database Archive that has three 100-MB data files and two 100-MB transaction log files. The primary file is the first file in the list and is explicitly specified with the PRIMARY keyword. The transaction log files are specified following the LOG ON keywords. Note the extensions used for the files in the FILENAME option: .mdf is used for primary data files, .ndf is used for the secondary data files, and .ldf is used for transaction log files. This example places the database on the D: drive instead of with the master database.

D. Create a database that has filegroups

The following example creates the database Sales that has the following filegroups:

• The primary filegroup with the files Spri1_dat and Spri2_dat . The FILEGROWTH increments for these files are specified as 15% .
• A filegroup named SalesGroup1 with the files SGrp1Fi1 and SGrp1Fi2 .
• A filegroup named SalesGroup2 with the files SGrp2Fi1 and SGrp2Fi2 .

This example places the data and log files on different disks to improve performance.

E. Attach a database

The following example detaches the database Archive created in example D, and then attaches it by using the FOR ATTACH clause. Archive was defined to have multiple data and log files. However, because the location of the files has not changed since they were created, only the primary file has to be specified in the FOR ATTACH clause. Beginning with SQL Server 2005 (9.x), any full-text files that are part of the database that is being attached will be attached with the database.

F. Create a database snapshot

The following example creates the database snapshot sales_snapshot0600 . Because a database snapshot is read-only, a log file cannot be specified. In conformance with the syntax, every file in the source database is specified, and filegroups are not specified.

The source database for this example is the Sales database created in example D.

G. Create a database and specify a collation name and options

The following example creates the database MyOptionsTest . A collation name is specified and the TRUSTYWORTHY and DB_CHAINING options are set to ON .

H. Attach a full-text catalog that has been moved

The following example attaches the full-text catalog AdvWksFtCat along with the AdventureWorks2022 data and log files. In this example, the full-text catalog is moved from its default location to a new location c:\myFTCatalogs . The data and log files remain in their default locations.

I. Create a database that specifies a row filegroup and two FILESTREAM filegroups

The following example creates the FileStreamDB database. The database is created with one row filegroup and two FILESTREAM filegroups. Each filegroup contains one file:

• FileStreamDB_data contains row data. It contains one file, FileStreamDB_data.mdf with the default path.
• FileStreamPhotos contains FILESTREAM data. It contains two FILESTREAM data containers, FSPhotos , located at C:\MyFSfolder\Photos and FSPhotos2 , located at D:\MyFSfolder\Photos . It is marked as the default FILESTREAM filegroup.
• FileStreamResumes contains FILESTREAM data. It contains one FILESTREAM data container, FSResumes , located at C:\MyFSfolder\Resumes .

J. Create a database that has a FILESTREAM filegroup with multiple files

The following example creates the BlobStore1 database. The database is created with one row filegroup and one FILESTREAM filegroup, FS . The FILESTREAM filegroup contains two files, FS1 and FS2 . Then the database is altered by adding a third file, FS3 , to the FILESTREAM filegroup.

• ALTER DATABASE
• Database Detach and Attach
• DROP DATABASE
• ALTER AUTHORIZATION
• sp_detach_db
• sp_removedbreplication
• Move Database Files
• Binary Large Object (Blob) Data

* SQL Database *

In Azure SQL Database, this statement can be used with an Azure SQL server to create a single database or a database in an elastic pool. With this statement, you specify the database name, collation, maximum size, edition, service objective, and, if applicable, the elastic pool for the new database. It can also be used to create the database in an elastic pool. Additionally, it can be used to create a copy of the database on another SQL Database server.

Create a database

Copy a database.

The name of the new database. This name must be unique on the SQL Server and comply with the SQL Server rules for identifiers. For more information, see Identifiers .

Collation_name

Specifies the default collation for the database data. Specify CATALOG_COLLATION for system metadata, such as object identifiers.

Collation name can be either a Windows collation name or a SQL collation name. If not specified, the database is assigned the default collation, which is SQL_Latin1_General_CP1_CI_AS.

For more information about the Windows and SQL collation names, COLLATE (Transact-SQL) .

CATALOG_COLLATION

Specifies the default collation for the metadata catalog. The CATALOG_COLLATION argument is only available during database creation and cannot be changed after creation.

By default, the metadata catalog for system object names is collated to SQL_Latin1_General_CP1_CI_AS collation. This is the default setting on Azure SQL Database if CATALOG_COLLATION is unspecified.

DATABASE_DEFAULT specifies that the metadata catalog used for system views and system tables be collated to match the collation for the database. If you desire that object identifiers in system metadata follow the same collation as data, you should create the database WITH CATALOG_COLLATION = DATABASE_DEFAULT .

You may desire different collations for data and object identifiers. The following example creates the database with a case-sensitive collation for row data, but will use the default SQL_Latin1_General_CP1_CI_AS case-insensitive collation for object identifiers.

If you desire that both data and system metadata use the same collation, specify WITH CATALOG_COLLATION = DATABASE_DEFAULT . The following example creates the database with a case-sensitive collation, which will be used for object identifiers.

BACKUP_STORAGE_REDUNDANCY = ['LOCAL' | 'ZONE' | 'GEO']

Specifies how the point-in-time restore and long-term retention backups for a database are replicated. Geo restore or ability to recover from regional outage is only available when database is created with GEO backup storage redundancy. Unless explicitly specified, databases created with T-SQL use geo-redundant backup storage.

To enforce data residency when you're creating a database by using T-SQL, use LOCAL or ZONE as input to the BACKUP_STORAGE_REDUNDANCY parameter.

Specifies the maximum size of the database. MAXSIZE must be valid for the specified EDITION (service tier).

Following are the supported MAXSIZE values and defaults (D) for the service tiers.

The MAXSIZE argument does not apply to single databases in the Hyperscale service tier. Hyperscale tier databases grow as needed, up to 100 TB. The SQL Database service adds storage automatically - you do not need to set a maximum size.

DTU model for single and pooled databases on a SQL Database server

* P11 and P15 allow MAXSIZE up to 4 TB with 1024 GB being the default size. P11 and P15 can use up to 4 TB of included storage at no additional charge. In the Premium tier, MAXSIZE greater than 1 TB is currently available in the following regions: US East2, West US, US Gov Virginia, West Europe, Germany Central, South East Asia, Japan East, Australia East, Canada Central, and Canada East. For additional details regarding resource limitations for the DTU model, see DTU resource limits .

The MAXSIZE value for the DTU model, if specified, has to be a valid value shown in the previous table for the service tier specified.

For limits such as maximum data size and tempdb size in the vCore purchasing model, refer to the articles for resource limits for single databases or resource limits for elastic pools .

If no MAXSIZE value is set when using the vCore model, the default is 32 GB. For additional details regarding resource limitations for vCore model, see vCore resource limits .

Specifies the service tier of the database.

Single and pooled databases. The available values are: 'Basic', 'Standard', 'Premium', 'GeneralPurpose', 'BusinessCritical', and 'Hyperscale'.

The following rules apply to MAXSIZE and EDITION arguments:

• If EDITION is specified but MAXSIZE is not specified, the default value for the edition is used. For example, if the EDITION is set to Standard, and the MAXSIZE is not specified, then the MAXSIZE is automatically set to 250 MB.
• If neither MAXSIZE nor EDITION is specified, the EDITION is set to GeneralPurpose , and MAXSIZE is set to 32 GB.

SERVICE_OBJECTIVE

Specifies the compute size and service objective.

• For DTU purchasing model: S0 , S1 , S2 , S3 , S4 , S6 , S7 , S9 , S12 , P1 , P2 , P4 , P6 , P11 , P15
• For example:
• GP_Gen5_8 for General Purpose Standard-series (Gen5) compute, 8 vCores.
• GP_S_Gen5_8 for General Purpose Serverless Standard-series (Gen5) compute, 8 vCores.
• HS_Gen5_8 for Hyperscale - provisioned compute - standard-series (Gen5), 8 vCores.

For service objective descriptions and more information about the size, editions, and the service objectives combinations, see Azure SQL Database Service Tiers . If the specified SERVICE_OBJECTIVE is not supported by the EDITION, you receive an error. To change the SERVICE_OBJECTIVE value from one tier to another (for example from S1 to P1), you must also change the EDITION value. Support for PRS service objectives has been removed.

ELASTIC_POOL (name = <elastic_pool_name>)

Applies to: Single and pooled databases only. Does not apply to databases in the Hyperscale service tier. To create a new database in an elastic database pool, set the SERVICE_OBJECTIVE of the database to ELASTIC_POOL and provide the name of the pool. For more information, see Create and manage a SQL Database elastic pool .

AS COPY OF [source_server_name.]source_database_name

Applies to: Single and pooled databases only. For copying a database to the same or a different SQL Database server.

source_server_name The name of the SQL Database server where the source database is located. This parameter is optional when the source database and the destination database are to be located on the same SQL Database server.

The AS COPY OF argument does not support the fully qualified unique domain names. In other words, if your server's fully qualified domain name is serverName.database.windows.net , use only serverName during database copy.

source_database_name

The name of the database that is to be copied.

Databases in Azure SQL Database have several default settings that are set when the database is created. For more information about these default settings, see the list of values in DATABASEPROPERTYEX .

MAXSIZE provides the ability to limit the size of the database. If the size of the database reaches its MAXSIZE , you receive error code 40544. When this occurs, you cannot insert or update data, or create new objects (such as tables, stored procedures, views, and functions). However, you can still read and delete data, truncate tables, drop tables and indexes, and rebuild indexes. You can then update MAXSIZE to a value larger than your current database size or delete some data to free storage space. There might be as much as a fifteen-minute delay before you can insert new data.

To change the size, edition, or service objective values later, use ALTER DATABASE (Azure SQL Database) .

Database Copies

Applies to: Single and pooled databases only.

Copying a database using the CREATE DATABASE statement is an asynchronous operation. Therefore, a connection to the SQL Database server is not needed for the full duration of the copy process. The CREATE DATABASE statement returns control to the user after the entry in sys.databases is created but before the database copy operation is complete. In other words, the CREATE DATABASE statement returns successfully when the database copy is still in progress.

• Monitoring the copy process on an SQL Database server: Query the percentage_complete or replication_state_desc columns in the dm_database_copies or the state column in the sys.databases view. The sys.dm_operation_status view can be used as well as it returns the status of database operations including database copy.

At the time the copy process completes successfully, the destination database is transactionally consistent with the source database.

The following syntax and semantic rules apply to your use of the AS COPY OF argument:

• The source server name and the server name for the copy target might be the same or different. When they are the same, this parameter is optional and the server context of the current session is used by default.
• The source and destination database names must be specified, unique, and comply with the SQL Server rules for identifiers. For more information, see Identifiers .
• The CREATE DATABASE statement must be executed within the context of the master database of the SQL Database server where the new database will be created.
• After the copying completes, the destination database must be managed as an independent database. You can execute the ALTER DATABASE and DROP DATABASE statements against the new database independently of the source database. You can also copy the new database to another new database.
• The source database might continue to be accessed while the database copy is in progress.

For more information, see Create a copy of an Azure SQL database using Transact-SQL .

By default, the database copy is created with the same backup storage redundancy as that of the source database.

To create a database, the login must be one of the following principals:

• The server-level principal login
• The Microsoft Entra administrator for the logical server in Azure
• A login that is a member of the dbmanager database role

Additional requirements for using CREATE DATABASE ... AS COPY OF syntax: The login executing the statement on the local server must also be at least the db_owner on the source server. If the login is based on SQL Server authentication, the login executing the statement on the local server must have a matching login on the source SQL Database server, with an identical name and password.

Simple example

A simple example for creating a database.

Simple example with edition

A simple example for creating a general purpose database.

Example with additional options

An example using multiple options.

Create a database copy

An example creating a copy of a database.

Create a database in an elastic pool

Creates new database in pool named S3M100:

Create a copy of a database on another logical server

The following example creates a copy of the db_original database named db_copy in the P2 compute size (service objective) for a single database. This is true regardless of whether db_original is in an elastic pool or a compute size (service objective) for a single database.

The following example creates a copy of the db_original database, named db_copy in an elastic pool named ep1 . This is true regardless of whether db_original is in an elastic pool or a compute size (service objective) for a single database. If db_original is in an elastic pool with a different name, then db_copy is still created in ep1 .

Create database with specified catalog collation value

The following example sets the catalog collation to DATABASE_DEFAULT during database creation, which sets the catalog collation to be the same as the database collation.

Create database using zone-redundancy for backups

The following example sets zone-redundancy for database backups. Both point-in-time restore backups and long-term retention backups (if configured) will use the same backup storage redundancy.

Create a ledger database

• sys.dm_database_copies - Azure SQL Database
• ALTER DATABASE (Azure SQL Database)
• DTU resource limits
• vCore resource limits for single databases
• vCore Resource limits for elastic pools

* SQL Managed Instance *

Azure SQL Managed Instance

In Azure SQL Managed Instance, this statement is used to create a database. When creating a database on a managed instance, you specify the database name and collation.

To add files or set containment for a database in a managed instance, use the ALTER DATABASE statement.

The name of the new database. This name must be unique on the SQL server and comply with the SQL Server rules for identifiers. For more information, see Identifiers .

Specifies the default collation for the database. Collation name can be either a Windows collation name or a SQL collation name. If not specified, the database is assigned the default collation, which is SQL_Latin1_General_CP1_CI_AS.

The CREATE DATABASE statement must be the only statement in a Transact-SQL batch.

The following are CREATE DATABASE limitations:

Files and filegroups cannot be defined.

WITH options are not supported, except for WITH LEDGER .

As workaround, use ALTER DATABASE . after CREATE DATABASE to set database options and to add files.

To create a database, a login must be one of the following:

• The Microsoft Entra administrator for the for the logical server in Azure
• A login that is a member of the dbcreator database role

Simple Example

• See ALTER DATABASE

* Azure Synapse Analytics *

In Azure Synapse, this statement can be used with an Azure SQL Database server to create a dedicated SQL pool. With this statement, you specify the database name, collation, maximum size, edition, and service objective.

• CREATE DATABASE is supported for standalone dedicated SQL pools (formerly SQL DW) using Gen2 service levels.
• CREATE DATABASE is not supported for dedicated SQL pools in an Azure Synapse Analytics workspace. Instead, use the Azure portal .
• CREATE DATABASE is supported for serverless SQL pools in Azure Synapse Analytics.
• Dedicated SQL pool
• Serverless SQL pool

The name of the new database. This name must be unique on the SQL server, which can host both databases in Azure SQL Database and Azure Synapse Analytics databases, and comply with the SQL Server rules for identifiers. For more information, see Identifiers .

collation_name

For more information about the Windows and SQL collation names, see COLLATE (Transact-SQL) .

The default is 245,760 GB (240 TB).

Applies to: Optimized for Compute Gen1

The maximum allowable size for the database. The database cannot grow beyond MAXSIZE.

Applies to: Optimized for Compute Gen2

The maximum allowable size for rowstore data in the database. Data stored in rowstore tables, a columnstore index's deltastore, or a nonclustered index on a clustered columnstore index cannot grow beyond MAXSIZE. Data compressed into columnstore format does not have a size limit and is not constrained by MAXSIZE.

Specifies the service tier of the database. For Azure Synapse Analytics use datawarehouse .

Specifies the compute size (service objective). The service levels for Gen2 are measured in compute data warehouse units (cDWU), for example DW2000c . Gen1 service levels are measured in DWUs, for example DW2000 . For more information about service objectives for Azure Synapse, see Data Warehouse Units (DWUs) . Gen1 service objectives (no longer listed) are no longer supported, you may receive an error: Azure SQL Data Warehouse Gen1 has been deprecated in this region. Please use SQL Analytics in Azure Synapse.

Use DATABASEPROPERTYEX to see the database properties.

Use ALTER DATABASE - Azure Synapse Analytics to change the max size, or service objective values later.

Azure Synapse is set to COMPATIBILITY_LEVEL 130 and cannot be changed. For more information, see Improved Query Performance with Compatibility Level 130 in Azure SQL Database .

Required permissions:

• Server level principal login, created by the provisioning process, or
• Member of the dbmanager database role.

Error handling

If the size of the database reaches MAXSIZE you will receive error code 40544. When this occurs, you cannot insert and update data, or create new objects (such as tables, stored procedures, views, and functions). You can still read and delete data, truncate tables, drop tables and indexes, and rebuild indexes. You can then update MAXSIZE to a value larger than your current database size or delete some data to free storage space. There might be as much as a fifteen-minute delay before you can insert new data.

Limitations and restrictions

You must be connected to the master database to create a new database.

You cannot change the database collation after the database is created.

Examples: Azure Synapse Analytics

A. simple example.

A simple example for creating a standalone dedicated SQL pool (formerly SQL DW). This creates the database with the smallest max size (10,240 GB), the default collation (SQL_Latin1_General_CP1_CI_AS), and the smallest Gen2 service objective (DW100c).

B. Create a data warehouse database with all the options

An example of creating a 10-terabyte standalone dedicated SQL pool (formerly SQL DW).

C. Simple example in a Synapse Analytics serverless SQL pool

This creates the database in the serverless pool, specifying a collation (Latin1_General_100_CI_AS_KS_WS).

• ALTER DATABASE (Azure Synapse Analytics)
• CREATE TABLE (Azure Synapse Analytics)

* Analytics Platform System (PDW) *

Analytics Platform System

In Analytics Platform System, this statement is used to create a new database on an Analytics Platform System appliance. Use this statement to create all files associated with an appliance database and to set maximum size and auto-growth options for the database tables and transaction log.

The name of the new database. For more information on permitted database names, see "Object Naming Rules" and "Reserved Database Names" in the Analytics Platform System (PDW) product documentation .

AUTOGROW = ON | OFF

Specifies whether the replicated_size , distributed_size , and log_size parameters for this database will automatically grow as needed beyond their specified sizes. Default value is OFF .

If AUTOGROW is ON, replicated_size , distributed_size , and log_size will grow as required (not in blocks of the initial specified size) with each data insert, update, or other action that requires more storage than has already been allocated.

If AUTOGROW is OFF, the sizes will not grow automatically. Analytics Platform System (PDW) will return an error when attempting an action that requires replicated_size , distributed_size , or log_size to grow beyond their specified value.

AUTOGROW is either ON for all sizes or OFF for all sizes. For example, it is not possible to set AUTOGROW ON for log_size , but not set it for replicated_size .

replicated_size [ GB ]

A positive number. Sets the size (in integer or decimal gigabytes) for the total space allocated to replicated tables and corresponding data on each Compute node . For minimum and maximum replicated_size requirements, see "Minimum and Maximum Values" in the Analytics Platform System (PDW) product documentation .

If AUTOGROW is ON, replicated tables will be permitted to grow beyond this limit.

If AUTOGROW is OFF, an error will be returned if a user attempts to create a new replicated table, insert data into an existing replicated table, or update an existing replicated table in a manner that would increase the size beyond replicated_size .

distributed_size [ GB ]

A positive number. The size, in integer or decimal gigabytes, for the total space allocated to distributed tables (and corresponding data) across the appliance . For minimum and maximum distributed_size requirements, see "Minimum and Maximum Values" in the Analytics Platform System (PDW) product documentation .

If AUTOGROW is ON, distributed tables will be permitted to grow beyond this limit.

If AUTOGROW is OFF, an error will be returned if a user attempts to create a new distributed table, insert data into an existing distributed table, or update an existing distributed table in a manner that would increase the size beyond distributed_size .

log_size [ GB ]

A positive number. The size (in integer or decimal gigabytes) for the transaction log across the appliance .

For minimum and maximum log_size requirements, see "Minimum and Maximum Values" in the Analytics Platform System (PDW) product documentation .

If AUTOGROW is ON, the log file is permitted to grow beyond this limit. Use the DBCC SHRINKLOG (Azure Synapse Analytics) statement to reduce the size of the log files to their original size.

If AUTOGROW is OFF, an error will be returned to the user for any action that would increase the log size on an individual Compute node beyond log_size .

Requires the CREATE ANY DATABASE permission in the master database, or membership in the sysadmin fixed server role.

Databases are created with database compatibility level 120, which is the compatibility level for SQL Server 2014 (12.x). This ensures that the database will be able to use all of the SQL Server 2014 (12.x) functionality that PDW uses.

Limitations and Restrictions

The CREATE DATABASE statement is not allowed in an explicit transaction. For more information, see Statements .

For information on minimum and maximum constraints on databases, see "Minimum and Maximum Values" in the Analytics Platform System (PDW) product documentation .

At the time a database is created, there must be enough available free space on each Compute node to allocate the combined total of the following sizes:

• SQL Server database with tables the size of replicated_table_size .
• SQL Server database with tables the size of ( distributed_table_size / number of Compute nodes).
• SQL Server logs the size of ( log_size / number of Compute nodes).

Takes a shared lock on the DATABASE object.

After this operation succeeds, an entry for this database will appear in the sys.databases and sys.objects metadata views.

Examples: Analytics Platform System (PDW)

A. basic database creation examples.

The following example creates the database mytest with a storage allocation of 100 GB per Compute node for replicated tables, 500 GB per appliance for distributed tables, and 100 GB per appliance for the transaction log. In this example, AUTOGROW is off by default.

The following example creates the database mytest with the same parameters as above, except that AUTOGROW is turned on. This allows the database to grow outside the specified size parameters.

B. Create a database with partial gigabyte sizes

The following example creates the database mytest , with AUTOGROW off, a storage allocation of 1.5 GB per Compute node for replicated tables, 5.25 GB per appliance for distributed tables, and 10 GB per appliance for the transaction log.

• ALTER DATABASE (Analytics Platform System)

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