Key Features of RDBMS

• Data Structure:

  • Data is stored in tables, where each table has a unique primary key.
  • Tables can be related to each other through foreign keys, establishing relationships between different data points.

• SQL Usage:

  • Most RDBMSs use Structured Query Language (SQL) for data manipulation and querying.
  • SQL allows users to perform operations such as creating, reading, updating, and deleting data (CRUD operations).

• ACID Properties:

  • RDBMSs ensure data integrity and reliability through ACID properties:
    • Atomicity: Transactions are all-or-nothing.
    • Consistency: Transactions bring the database from one valid state to another.
    • Isolation: Transactions are executed independently.
    • Durability: Once a transaction is committed, it remains so, even in the event of a system failure.

Advantages of RDBMS

• Data Integrity:

  • RDBMSs enforce data integrity through constraints like primary keys, foreign keys, and unique constraints.

• Flexibility:

  • Users can easily add new data or modify existing tables without disrupting the overall database structure.

• Security:

  • RDBMSs provide robust security features, allowing administrators to control access to data based on user roles.

• Scalability:

  • RDBMSs can handle large volumes of data and support multiple concurrent users.

Common RDBMS Examples

• MySQL: An open-source RDBMS widely used for web applications.
• PostgreSQL: An advanced open-source RDBMS known for its robustness and support for complex queries.
• Oracle Database: A commercial RDBMS used in large enterprises, particularly in banking and finance.
• Microsoft SQL Server: A widely used RDBMS in enterprise environments, offering various editions for different needs.

Use Cases of RDBMS

• Business Applications: RDBMSs are commonly used in applications that require structured data management, such as customer relationship management (CRM) systems and enterprise resource planning (ERP) systems.
• E-commerce: They manage product inventories, customer orders, and transaction records.
• Banking: RDBMSs handle account information, transactions, and customer data securely and reliably.

1. Early Database Systems (1960s)

• Hierarchical and Network Models: Before the relational model, databases were primarily hierarchical (e.g., IBM's IMS) or network-based (e.g., CODASYL). These models had limitations in terms of flexibility and complexity.

2. Introduction of the Relational Model (1970)

• E.F. Codd's Paper: In 1970, Edgar F. Codd, a computer scientist at IBM, published a seminal paper titled "A Relational Model of Data for Large Shared Data Banks." This paper introduced the concept of the relational model, where data is organized into tables (relations) with relationships defined by keys.

3. Development of RDBMS (1970s)

• System R: IBM developed System R, one of the first RDBMS implementations, which demonstrated the feasibility of Codd's model and introduced SQL (Structured Query Language) as a way to query and manipulate data.
• Ingres: Another early RDBMS, developed at the University of California, Berkeley, which also contributed to the development of SQL.

4. Commercialization (1980s)

• First Commercial RDBMS: In the late 1970s and early 1980s, several companies began to commercialize RDBMS technology. Notable examples include Oracle Database (released in 1979) and IBM's DB2 (released in 1983).
• SQL Standardization: The American National Standards Institute (ANSI) standardized SQL in 1986, which helped establish it as the standard language for RDBMS.

5. Growth and Maturity (1990s)

• Widespread Adoption: RDBMSs became the dominant database technology for businesses, with systems like Microsoft SQL Server, Sybase, and PostgreSQL gaining popularity.
• Advancements in Features: RDBMSs began to incorporate features such as support for transactions, improved concurrency control, and enhanced security measures.

6. The Rise of Object-Relational Databases (Late 1990s)

• Object-Relational Models: As applications became more complex, the need for more advanced data types and relationships led to the development of object-relational databases, which combined features of RDBMSs and object-oriented programming.

7. The Era of Big Data and NoSQL (2000s)

• Emergence of NoSQL: With the rise of big data and the need for handling unstructured data, NoSQL databases gained popularity. While this posed challenges to traditional RDBMSs, many systems adapted by incorporating NoSQL features or creating hybrid models.

8. Modern Developments (2010s - Present)

• Cloud-Based RDBMS: The advent of cloud computing led to the development of cloud-based RDBMS solutions (e.g., Amazon RDS, Google Cloud SQL) that allow for scalable and flexible database management.
• Integration with Big Data Technologies: Many RDBMSs have integrated with big data technologies (like Apache Hadoop) and support for JSON and other unstructured data formats.

1. Database: A structured collection of data that is stored and accessed electronically.

2. Table: A set of data elements (values) that is organized using a model of vertical columns and horizontal rows. Each table represents a different entity.

3. Row (Record): A single entry in a table. Each row contains data related to a specific instance of the entity represented by the table.

4. Column (Field): A set of data values of a particular type, one for each row of the table. Each column represents an attribute of the entity.

5. Primary Key: A unique identifier for a record in a table. It ensures that no two rows have the same value for that key.

6. Foreign Key: A field (or collection of fields) in one table that uniquely identifies a row of another table or the same table. It establishes a relationship between the two tables.

7. Schema: The structure that defines the organization of data, including tables, columns, data types, and relationships among tables.

8. Normalization: The process of organizing data to reduce redundancy and improve data integrity. It involves dividing a database into tables and defining relationships between them.

9. Denormalization: The process of combining tables to improve read performance at the expense of write performance and data integrity.

10. SQL (Structured Query Language): A standard programming language used to manage and manipulate relational databases.

11. Index: A database object that improves the speed of data retrieval operations on a database table at the cost of additional space and slower write operations.

12. Transaction: A sequence of one or more SQL operations that are executed as a single unit of work. Transactions ensure data integrity and consistency.

13. ACID Properties: A set of properties (Atomicity, Consistency, Isolation, Durability) that guarantee that database transactions are processed reliably.

14. View: A virtual table that is based on the result of a SELECT query. It can simplify complex queries and provide a layer of security.

15. Stored Procedure: A set of SQL statements that can be stored in the database and executed as a single unit. It can accept parameters and return results.

16. Trigger: A special type of stored procedure that automatically executes in response to certain events on a particular table or view.

17. Join: An operation that combines rows from two or more tables based on a related column between them.

18. Subquery: A query nested inside another query. It can be used to retrieve data that will be used in the main query.

19. Data Integrity: The accuracy and consistency of data within a database. It can be ensured through constraints, such as primary keys, foreign keys, and unique constraints.

20. Backup and Recovery: Processes and strategies for creating copies of data to prevent data loss and restoring data from those copies when necessary.