Database Design can be defined as a set of procedures or collection of tasks involving various steps taken to implement a database.A good database design is important. It helps you get the right information when you need it.
- 1. AD3391 Database Design and Management
- 2. COURSE OBJECTIVES: • To introduce database development life cycle and conceptual modeling • To learn SQL for data definition, manipulation and querying a database • To learn relational database design using conceptual mapping and normalization • To learn transaction concepts and serializability of schedules • To learn data model and querying in object-relational and No-SQL databases
- 3. Contd.. 1. Database Development Life Cycle & Conceptual Modeling Simple Explanation: This is like planning before building a house. You figure out what the database needs to do, how it will be used, and draw a "blueprint" (called a conceptual model) of how data will be stored and related.
- 4. 2. Learning SQL for Data Definition, Manipulation, and Querying Simple Explanation: SQL (Structured Query Language) is the language used to talk to databases. Data definition: Creating tables and structures Data manipulation: Adding, changing, or deleting data Contd..
- 5. 3. Relational Database Design Using Conceptual Mapping & Normalization Simple Explanation: This is about designing a clean and efficient database. Conceptual mapping: Turning your blueprint (model) into real database tables Normalization: Organizing data to reduce repetition and avoid errors Contd..
- 6. 4. Transaction Concepts and Serializability of Schedules Simple Explanation: A transaction is a complete task (like transferring money between bank accounts). Serializability means making sure that even if many users use the database at once, the result is still correct — as if they each used it one after another. Contd..
- 7. 5. Data Models and Querying in Object-Relational and NoSQL Databases Simple Explanation: Not all databases look like tables: Object-relational databases are like regular databases but allow more complex data (e.g., images or videos) and relationships. NoSQL databases don’t use tables. They're great for big, fast-changing data like social media or real-time apps. You’ll learn how to store and get data from these types too. Contd..
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- 9. After the completion of this course, students will be able to: CO1:Understand the database development life cycle and apply conceptual modeling CO2:Apply SQL and programming in SQL to create, manipulate and query the database CO3:Apply the conceptual-to-relational mapping and normalization to design relational database CO4:Determine the serializability of any non-serial schedule using concurrency techniques CO5:Apply the data model and querying in Object-relational and No-SQL databases. Contd..
- 10. CO1: Understand the database development life cycle and apply conceptual modeling Simple Explanation: You will learn how to plan and design a database step-by-step. Just like an architect designs a building, you’ll learn to draw a data blueprint before building the actual database. Contd..
- 11. CO2: Apply SQL and programming in SQL to create, manipulate, and query the database Simple Explanation: You will be able to use SQL commands to: Create tables (like making folders) Insert and update data (like adding or editing files) Ask questions to the database (called queries) and get useful answers Contd..
- 12. CO3: Apply the conceptual-to-relational mapping and normalization to design relational database Simple Explanation: You’ll learn how to turn a paper design into real database tables and then organize data neatly so it avoids repetition, saves space, and prevents mistakes. Contd..
- 13. CO4: Determine the serializability of any non-serial schedule using concurrency techniques Simple Explanation: You’ll understand how multiple users can safely use a database at the same time without causing errors — like how ATMs work without mixing up your balance with someone else’s.
- 14. CO5: Apply the data model and querying in Object-relational and No-SQL databases Simple Explanation: You will be able to work with modern databases that store complex data (like images or videos) and big real-time data (like social media posts), and ask questions (queries) in those systems too. Contd..
- 15. Data Data is a word we hear everywhere nowadays. In general, data is a collection of facts, information, and statistics and this can be in various forms such as numbers, text, sound, images, or any other format.
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- 18. What is Database? A database is an organized collection of data stored electronically. It allows users and applications to easily access, update, and manipulate information. This data contains text, numbers, images, videos and more. Databases are managed using specialized software known as a Database Management System (DBMS), which facilitates the storage, retrieval, and manipulation of data. Databases are fundamental to modern computing, supporting various applications from online shopping platforms to social media networks. It is widely used in business, government, healthcare, and many other sectors to store and manage data, enabling informed decision-making and efficient operations.
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- 25. Why do we use database Efficient Data Storage: Databases can store large amounts of data and ensure it remains accessible and organized. Facilitate Transactions: They enable smooth transactions in online banking, shopping, social media, and more by processing and retrieving data in real- time. Data Updates: Databases allow for easy and quick updates to data, ensuring that the most current information is always available. Data Analysis: Databases provide an excellent framework for analyzing trends and making data-driven decisions.
- 26. Applications of Databases Databases are essential part of our life. There are several everyday activities that involve our interaction with databases. Banking: Databases store financial transactions, account information and customer details. Transportation: Railway stations and airlines use databases to manage bookings, schedules, and ticketing systems. Education: Schools use databases to store student records, grades, and class schedules. Retail: Grocery stores, e-commerce platforms, and other businesses use databases to track inventory, customer orders, and transactions. Social Media & Communication: Platforms like WhatsApp, Gmail, and social media websites use databases to store user data, messages, photos, and interaction history. Multimedia Databases: With advancements in technology, databases now handle not only text data but also multimedia files like Images, Audio and Video Business: They allow businesses to analyze customer behavior, sales trends, and operational performance, leading to better strategies and decisions. Data Science: Companies apply data science techniques to the stored data in databases to identify patterns, make predictions, and optimize business processes.
- 27. Key Features of DBMS Data Modeling: Tools to create and modify data models, defining the structure and relationships within the database. Data Storage and Retrieval: Efficient mechanisms for storing data and executing queries to retrieve it quickly. Concurrency Control: Ensures multiple users can access the database simultaneously without conflicts. Data Integrity and Security: Enforces rules to maintain accurate and secure data, including access controls and encryption. Backup and Recovery: Protects data with regular backups and enables recovery in case of system failures.
- 28. Advantages of DBMS Data organization: A DBMS allows for the organization and storage of data in a structured manner, making it easy to retrieve and query the data as needed. Data integrity: A DBMS provides mechanisms for enforcing data integrity constraints, such as constraints on the values of data and access controls that restrict who can access the data. Concurrent access: A DBMS provides mechanisms for controlling concurrent access to the database, to ensure that multiple users can access the data without conflicting with each other. Data security: A DBMS provides tools for managing the security of the data, such as controlling access to the data and encrypting sensitive data. Backup and recovery: A DBMS provides mechanisms for backing up and recovering the data in the event of a system failure. Data sharing: A DBMS allows multiple users to access and share the same data, which can be useful in a collaborative work environment.
- 29. Advantages of DBMS Disadvantages of DBMS Complexity: DBMS can be complex to set up and maintain, requiring specialized knowledge and skills. Performance overhead: The use of a DBMS can add overhead to the performance of an application, especially in cases where high levels of concurrency are required. Scalability: The use of a DBMS can limit the scalability of an application, since it requires the use of locking and other synchronization mechanisms to ensure data consistency. Cost: The cost of purchasing, maintaining and upgrading a DBMS can be high, especially for large or complex systems. Limited Use Cases: Not all use cases are suitable for a DBMS, some solutions don't need high reliability, consistency or security and may be better served by other types of data storage.
- 31. Relational Database Management System (RDBMS) RDBMS organizes data into tables (relations) composed of rows and columns. It uses primary keys to uniquely identify rows and foreign keys to establish relationships between tables. Queries are written in SQL (Structured Query Language), which allows for efficient data manipulation and retrieval. Examples: MySQL oracle, Microsoft SQL Server and Postgre SQL.
- 32. NoSQL DBMS 2. NoSQL systems are designed to handle large-scale data and provide high performance for scenarios where relational models might be restrictive. They store data in various non-relational formats, such as key-value pairs, documents, graphs or columns. These flexible data models enable rapid scaling and are well-suited for unstructured or semi-structured data. Examples: MongoDB, Cassandra, DynamoDB and Redis.
- 33. 3. Object-Oriented DBMS (OODBMS) OODBMS integrates object-oriented programming concepts into the database environment, allowing data to be stored as objects. This approach supports complex data types and relationships, making it ideal for applications requiring advanced data modeling and real-world simulations. Examples: ObjectDB, db4o.
- 34. 4. Hierarchical Database A hierarchical database organizes data in a tree-like structure, where each record (node) has a single parent and can have multiple children. This model is similar to a file system with folders and subfolders. It is efficient for storing data with a clear hierarchy, such as organizational charts or file directories. Navigation is fast and predictable due to the fixed structure. However, it lacks flexibility and is difficult to restructure or handle complex many-to-many relationships. Example: IBM Information Management System (IMS).
- 35. 5. Network Database A network database uses a graph-like model to allow more complex relationships between entities. Unlike the hierarchical model, it permits each child to have multiple parents, enabling many-to-many relationships. Data is represented using records and sets, where sets define the relationships. This model is more flexible than the hierarchical model and better suited for applications with complex data linkages. However, it can be complicated to design and maintain. Example: Integrated Data Store (IDS), TurboIMAGE.
- 36. Cloud-Based Database Cloud-based databases are hosted on cloud computing platforms like AWS, Azure or Google Cloud. They offer on-demand scalability, high availability, automatic backups and remote accessibility. These databases can be relational (SQL) or non-relational (NoSQL) and are maintained by cloud service providers, reducing administrative overhead. They support modern application requirements, including distributed access and real-time analytics. Security and data privacy are managed through built-in compliance and encryption features. Example: Amazon RDS (for SQL), MongoDB Atlas (for NoSQL), Google BigQuery.
- 37. Database Languages Database languages are specialized sets of commands and instructions used to define, manipulate and control data within a database. Each language type plays a distinct role in database management, ensuring efficient storage, retrieval and security of data. The primary database languages include:
- 38. 1. Data Definition Language (DDL) DDL is the short name for Data Definition Language, which deals with database schemas and descriptions, of how the data should reside in the database. CREATE: to create a database and its objects like (table, index, views, store procedure, function and triggers) ALTER: alters the structure of the existing database DROP: delete objects from the database TRUNCATE: remove all records from a table, including all spaces allocated for the records are removed COMMENT: add comments to the data dictionary RENAME: rename an object
- 39. 2. Data Manipulation Language (DML) DML focuses on manipulating the data stored in the database, enabling users to retrieve, add, update and delete data. SELECT: retrieve data from a database INSERT: insert data into a table UPDATE: updates existing data within a table DELETE: Delete all records from a database table MERGE: UPSERT operation (insert or update) CALL: call a PL/SQL or Java subprogram EXPLAIN PLAN: interpretation of the data access path LOCK TABLE: concurrency Control
- 40. 3. Data Control Language (DCL) DCL commands manage access permissions, ensuring data security by controlling who can perform certain actions on the database. GRANT: Provides specific privileges to a user (e.g., SELECT, INSERT). REVOKE: Removes previously granted permissions from a user.
- 41. 4. Transaction Control Language (TCL) TCL commands oversee transactional data to maintain consistency, reliability and atomicity. ROLLBACK: Undoes changes made during a transaction. COMMIT: Saves all changes made during a transaction. SAVEPOINT: Sets a point within a transaction to which one can later roll back.
- 42. 5. Data Query Language (DQL) DQL is a subset of DML, specifically focused on data retrieval. SELECT: The primary DQL command, used to query data from the database without altering its structure or contents.
- 43. Database Environment One of the primary aims of a database is to supply users with an abstract view of data, hiding a certain element of how data is stored and manipulated. Therefore, the starting point for the design of a database should be an abstract and general description of the information needs of the organization that is to be represented in the database. And hence you will require an environment to store data and make it work as a database. In this chapter, you will learn about the database environment and its architecture.
- 44. Contd.. A database environment is a collective system of components that comprise and regulates the group of data, management, and use of data, which consist of software, hardware, people, techniques of handling database, and the data also. Here, the hardware in a database environment means the computers and computer peripherals that are being used to manage a database, and the software means the whole thing right from the operating system (OS) to the application programs that include database management software like M.S. Access or SQL Server. Again the people in a database environment include those people who administrate and use the system. The techniques are the rules, concepts, and instructions given to both the people and the software along with the data with the group of facts and information positioned within the database environment.
- 45. ANSI_SPARC Architecture of the database system: An early proposal for a standard terminology and general architecture for database systems was produced in 1971 by the DBTG (Data Base Task Group) appointed by the Conference on Data Systems and Languages (CODASYL, 1971). The DBTG recognized the need for a two-level approach with a system view called the schema and user views called sub-schemas.
- 46. The levels form a three-level architecture that includes an external, a conceptual, and an internal level. The way users recognize the data is called the external level. The way the DBMS and the operating system distinguish the data is the internal level, where the data is stored using the data structures and file. The conceptual level offers both the mapping and the desired independence between the external and internal levels.
- 48. Levels in the Architecture: 1. External Level (User Views) Shown at the top with "View 1", "View 2", ..., "View n". This level represents the user’s view of the database. Each user sees only the data they need in a customized form. Different users can have different views (subset of the database). Example: A sales manager sees sales data, while an HR manager sees employee records.
- 49. 2. Conceptual Level (Conceptual Schema) This is the community view of the entire database structure. It represents the logical structure of all data in the database. Independent of how data is stored or how users view it. Includes relationships, constraints, and data types. Acts as a bridge between users and the physical data.
- 50. . Internal Level (Internal Schema) Deals with how data is physically stored in the system. Describes the physical structure: files, indexing, compression, etc. This is managed by the DBMS for optimal storage and retrieval.
- 51. Physical Data Organization The actual database (DB) at the bottom. Data is stored in binary format on storage devices (like HDD, SSD).
- 52. DBMS Architecture 1-level, 2-Level, 3-Level Types of DBMS Architecture There are several types of DBMS Architecture that we use according to the usage requirements. 1-Tier Architecture 2-Tier Architecture 3-Tier Architecture
- 53. 1-Tier Architecture In 1-Tier Architecture, the user works directly with the database on the same system. This means the client, server and database are all in one application. The user can open the application, interact with the data and perform tasks without needing a separate server or network connection.
- 54. Contd.. A common example is Microsoft Excel. Everything from the user interface to the logic and data storage happens on the same device. The user enters data, performs calculations and saves files directly on their computer. This setup is simple and easy to use, making it ideal for personal or standalone applications. It does not require a network or complex setup, which is why it's often used in small-scale or individual use cases. This architecture is simple and works well for personal, standalone applications where no external server or network connection is needed.
- 55. Advantages of 1-Tier Architecture Below mentioned are the advantages of 1-Tier Architecture. Simple Architecture: 1-Tier Architecture is the most simple architecture to set up, as only a single machine is required to maintain it. Cost-Effective: No additional hardware is required for implementing 1-Tier Architecture, which makes it cost-effective. Easy to Implement: 1-Tier Architecture can be easily deployed and hence it is mostly used in small projects.
- 56. Disadvantages of 1-Tier Architecture Limited to Single User: Only one person can use the application at a time. It’s not designed for multiple users or teamwork. Poor Security: Since everything is on the same machine, if someone gets access to the system, they can access both the data and the application easily. No Centralized Control: Data is stored locally, so there's no central database. This makes it hard to manage or back up data across multiple devices. Hard to Share Data: Sharing data between users is difficult because everything is stored on one computer.
- 57. 2-Tier Architecture The 2-tier architecture is similar to a basic client-server model. The application at the client end directly communicates with the database on the server side. APIs like ODBC and JDBC are used for this interaction. The server side is responsible for providing query processing and transaction management functionalities.
- 59. On the client side, the user interfaces and application programs are run. The application on the client side establishes a connection with the server side to communicate with the DBMS. For Example: A Library Management System used in schools or small organizations is a classic example of two-tier architecture. Client Layer (Tier 1): This is the user interface that library staff or users interact with. For example they might use a desktop application to search for books, issue them, or check due dates. Database Layer (Tier 2): The database server stores all the library records such as book details, user information and transaction logs. The client layer sends a request (like searching for a book) to the database layer which processes it and sends back the result. This separation allows the client to focus on the user interface, while the server handles data storage and retrieval.
- 60. Advantages of 2-Tier Architecture Easy to Access: 2-Tier Architecture makes easy access to the database, which makes fast retrieval. Scalable: We can scale the database easily, by adding clients or upgrading hardware. Low Cost: 2-Tier Architecture is cheaper than 3-Tier Architecture and Multi-Tier Architecture. Easy Deployment: 2-Tier Architecture is easier to deploy than 3-Tier Architecture. Simple: 2-Tier Architecture is easily understandable as well as simple because of only two components.
- 61. Disadvantages of 2-Tier Architecture Limited Scalability: As the number of users increases, the system performance can slow down because the server gets overloaded with too many requests. Security Issues: Clients connect directly to the database, which can make the system more vulnerable to attacks or data leaks. Tight Coupling: The client and the server are closely linked. If the database changes, the client application often needs to be updated too. Difficult Maintenance: Managing updates, fixing bugs, or adding features becomes harder when the number of users or systems increases.
- 62. 3-Tier Architecture In 3-Tier Architecture, there is another layer between the client and the server. The client does not directly communicate with the server. Instead, it interacts with an application server which further communicates with the database system and then the query processing and transaction management takes place. This intermediate layer acts as a medium for the exchange of partially processed data between the server and the client. This type of architecture is used in the case of large web applications.
- 64. Example: E-commerce Store User: You visit an online store, search for a product and add it to your cart. Processing: The system checks if the product is in stock, calculates the total price and applies any discounts. Database: The product details, your cart and order history are stored in the database for future reference.
- 65. Advantages of 3-Tier Architecture Enhanced scalability: Scalability is enhanced due to the distributed deployment of application servers. Now, individual connections need not be made between the client and server. Data Integrity: 3-Tier Architecture maintains Data Integrity. Since there is a middle layer between the client and the server, data corruption can be avoided/removed. Security: 3-Tier Architecture Improves Security. This type of model prevents direct interaction of the client with the server thereby reducing access to unauthorized data.
- 66. DATABASE ENVIRONMENT One of the primary aims of a database is to supply users with an abstract view of data, hiding certain elements of how data is physically stored and manipulated. Therefore, the starting point for designing a database should be an abstract and general description of the information needs of the organization. To do this, we require an environment capable of storing data and managing it as a database.
- 67. VIEWS OF DATA / DATA ABSTRACTION A major purpose of a database system is to provide users with a simplified, abstract view of the data. In other words, the system hides internal storage and maintenance details from users.
- 68. Disadvantages of 3-Tier Architecture More Complex: 3-Tier Architecture is more complex in comparison to 2-Tier Architecture. Communication Points are also doubled in 3-Tier Architecture. Difficult to Interact: It becomes difficult for this sort of interaction to take place due to the presence of middle layers. Slower Response Time: Since the request passes through an extra layer (application server), it may take more time to get a response compared to 2- Tier systems. Higher Cost: Setting up and maintaining three separate layers (client, server and database) requires more hardware, software and skilled people. This makes it more expensive.
- 69. Conceptual Data Modeling Conceptual Data Modeling is the first step in database design that focuses on high-level, abstract representation of organizational data. It describes what data is stored, what the relationships are, and what the business rules are, without worrying about how the data is stored in a database system.
- 70. contd.. Definition: Conceptual Data Modeling is the process of creating a model that shows the entities, attributes, and relationships in a domain, representing real-world objects and their interactions.
- 71. Main Components: Component Description Entity A real-world object or concept (e.g., Student, Book, Order) Attribute A property of an entity (e.g., Name, Date of Birth) Relationship Association between entities (e.g., Student borrows Book)
- 72. THREE-SCHEMA ARCHITECTURE This model separates the database system into three levels, providing data abstraction and helping manage data independence. The three levels are:
- 73. Purpose: To understand the data requirements of an organization. To communicate business rules and structure clearly to stakeholders (both technical and non-technical). To serve as a blueprint for creating logical and physical data models.
- 74. (i) Internal Level (Physical Level) Describes how the data is physically stored on the hardware. Uses physical data models. Includes details such as: File structures Indexing Access paths Data blocks and pages
- 75. CustomerID Name Phone 101 Ananya 9876543210 102 Karthik 8765432198
- 81. What is a Schema in a Database? A schema is the blueprint or structure of a database. It defines how data is organized and how the relationships among data are handled. Formal Definition: A database schema is the logical design of a database. It describes: The tables (relations) The columns (attributes) in each table The data types The relationships between tables Constraints like primary keys, foreign keys, and uniqueness
- 82. The Database Development Life Cycle (DDLC) is a structured process followed to design, develop, and maintain a database system. It includes several stages to ensure that the database meets user requirements, is efficient, and is scalable.
- 83. 1. Requirements Analysis Goal: Understand what data needs to be stored, how it will be used, and what the users expect. Activities: Interview stakeholders (users, managers). Gather data requirements and constraints. Define the scope of the database. Output: Requirement specification document.
- 84. 2. Conceptual Design Goal: Create a high-level data model. Activities: Identify entities, attributes, and relationships. Draw Entity-Relationship Diagrams (ERD). Output: Conceptual schema (ER Diagram).
- 85. 3. Logical Design Goal: Convert the conceptual model into a logical model based on a database system (e.g., relational). Activities: Define tables, columns, keys, and constraints. Normalize tables to reduce redundancy. Output: Logical schema (relational model).
- 86. 5. Implementation Goal: Create the actual database using a DBMS (e.g., PostgreSQL, MySQL). Activities: Execute SQL commands to create the schema. Load sample or real data. Write procedures, triggers, views if needed. Output: Working database.
- 87. 6. Testing and Evaluation Goal: Ensure the database works correctly and efficiently. Activities: Test queries, reports, constraints, and performance. Validate data integrity and security. Output: Tested and validated database.
- 88. 7. Operation and Maintenance Goal: Keep the database running and updated. Activities: Monitor performance and security. Back up data regularly. Update structure or data as needed. Output: Maintained and updated database system.
- 89. Example: Library Management System 1. Requirements Analysis Goal: Understand what data the library needs to store. Requirements: Keep records of books, members, and loans. Each book has a title, author, and ISBN. Members can borrow multiple books. Track borrow and return dates. 📝 Output: Requirement Specification Document.
- 90. 2. Conceptual Design Goal: Identify entities, attributes, and relationships. Entities: •Book (BookID, Title, Author, ISBN) •Member (MemberID, Name, Email) •Loan (LoanID, BookID, MemberID, LoanDate, ReturnDate) Relationships: •A Member borrows many Books. •A Book can be borrowed many times. 🧩 Output: ER Diagram.
- 92. INSERT INTO Book VALUES (1, 'Harry Potter', 'J.K. Rowling', '9780747532743'); INSERT INTO Member VALUES (101, 'Alice', 'alice@example.com'); INSERT INTO Loan VALUES (1, 1, 101, '2025-08-01', NULL);
- 93. 6. Testing and Evaluation Goal: Ensure everything works. •Test queries like: sql SELECT Name, Title FROM Loan JOIN Book ON Loan.BookID = Book.BookID JOIN Member ON Loan.MemberID = Member.MemberID; •Check constraints (e.g., foreign key rules, unique emails). ✅ Output: Verified database.
- 94. 7. Operation and Maintenance Goal: Keep the system running. Schedule backups. Add new features (e.g., track fines). Tune queries for performance. 🔄 Output: Updated, reliable system.