Network Databases (e.g., CODASYL): Offered more complex, many-to-many relationships, improving flexibility over hierarchical models but still intricate to manage and query.
3. The Relational Revolution (1970s-1990s):
Edgar F. Codd's seminal paper in 1970 introduced the Relational Model, a theoretical breakthrough that simplified data organization. Data was stored in tables (relations) with rows and columns, and relationships were defined through shared keys.
Structured Query Language (SQL): The development of SQL provided a powerful, declarative language for querying and manipulating relational data, making databases accessible to a broader range of users.
ACID Properties: Relational Database Management Systems (RDBMS) emphasized Atomicity, Consistency, Isolation, and Durability (ACID) to ensure data integrity and reliability, especially for transactional workloads.
Dominance: RDBMS like Oracle, IBM Db2, and later MySQL and chinese overseas australia data Microsoft SQL Server became the industry standard, powering everything from banking systems to enterprise resource planning (ERP) applications for decades. Their structured nature, strong consistency guarantees, and standardized query language made them suitable for a vast majority of business needs.
The Data Explosion and the Cracks in the Relational Armor: Paving the Way for Special Databases
The turn of the millennium and the rapid proliferation of the internet, mobile devices, and subsequently, big data, revealed the limitations of purely relational systems.
1. The Web Scale Challenge:
The rigid schemas of RDBMS struggled to adapt to the rapidly evolving data models of web applications.
Vertical scaling (adding more power to a single server) became increasingly expensive and eventually hit physical limits in the face of massive user bases and data volumes.
Relational databases were not inherently designed for distributed architectures.
Traditional RDBMS were optimized for structured, tabular data. However, the internet generated vast amounts of unstructured data (text, images, video, audio) and semi-structured data (JSON, XML). Storing this efficiently in a relational model often involved complex and unnatural mappings.
3. The Need for Speed and Real-time Processing: