INTRODUCTION

DATA MODELS

RELATIONAL LANGUAGE AND RELATIONAL MODEL

DATABASE CONSTRAINTS AND NORMALIZATION

FILE STRUCTURE AND HASHING

QUERY PROCESSING AND OPTIMIZATION

TRANSACTION PROCESSING AND CONCURRENCY CONTROL-DBMS

CRASH RECOVERY

ADVANCED DATABASE CONCEPT

PRACTICAL-LAB WORK DBMS

PRACTICE QUESTION - ER DIAGRAM

OLD QUESTION BANK-DBMS-IOE

Concept and Evolution of Database

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.

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