Objectives of DBMS (Database Management System):
- Data Organization: One of the primary objectives of a DBMS is to organize data in a structured manner. It provides a framework for defining, storing, and retrieving data efficiently.
- Data Integrity: DBMS ensures the accuracy and consistency of data by enforcing integrity constraints. This includes rules that prevent invalid data entry and maintain the quality of the stored information.
- Data Security: Security is a critical objective of DBMS. It controls access to the database, ensuring that only authorized users can view or modify data. This involves user authentication, authorization, and encryption of sensitive information.
- Concurrency Control: DBMS manages concurrent access to the database by multiple users or applications. It ensures that transactions occur in isolation, preserving the consistency of the database.
- Data Independence: DBMS provides a layer of abstraction between the physical storage of data and the applications that use it. This allows changes in the database structure without affecting the applications that interact with it.
- Query Language Support: DBMS provides a query language (such as SQL) that enables users to interact with the database by querying and manipulating data. This facilitates easy and efficient data retrieval.
- Backup and Recovery: DBMS includes mechanisms for data backup and recovery in case of system failures, ensuring data availability and preventing data loss.
- Scalability: DBMS should be scalable to handle growing amounts of data and increasing user loads. It should be able to adapt to changing requirements without significant performance degradation.
Evolution of DBMS:
- File-Based Systems (1960s): In the early days of computing, data was managed using file-based systems. Each application had its own set of files, leading to redundancy and data inconsistency.
- Hierarchical and Network Models (1960s-1970s): These models were an improvement over file-based systems and introduced relationships between entities. However, they were complex and lacked flexibility.
- Relational Model (1970s): The relational model, introduced by E.F. Codd revolutionized database management. It introduced the concept of tables, rows, and columns, and established relationships between tables. SQL (Structured Query Language) became the standard language for interacting with relational databases.
- Object-Oriented Databases (1980s-1990s): This model extended the relational model to handle complex data types and relationships, accommodating object-oriented programming concepts.
- Client-Server Architecture (1980s-1990s): The emergence of client-server architecture allowed for distributed databases, with a separation between the database server and client applications.
- Relational Database Management Systems (RDBMS) Dominance (1980s-Present): RDBMS, such as Oracle, MySQL, and Microsoft SQL Server, became dominant due to their simplicity, flexibility, and wide adoption.
- NoSQL Databases (2000s-Present): With the rise of big data and the need for handling unstructured data, NoSQL databases (e.g., MongoDB, Cassandra) emerged. They offer flexible data models and horizontal scalability.
- Cloud-Based Databases (2010s-Present): Cloud computing has influenced the evolution of DBMS, with databases being hosted on cloud platforms like AWS, Azure, and Google Cloud. This allows for scalable and cost-effective database solutions.