Operational database definition
An operational database manages and stores data related to daily business activities, such as orders and sales leads. It facilitates real-time data processing and supports functions like inventory tracking.
See also: real-time data
Operational database examples
- Order processing databases store and manage customer orders, shipment details, and billing information.
- Inventory management databases track products, quantities, warehouse locations, and restocking needs.
- Banking databases hold customer accounts, transactions, loans, and other banking operations.
- Customer relationship management (CRM) databases keep track of customer interactions, sales leads, and marketing campaigns.
- Human resources databases store employee data and other HR-related information.
Business uses of operational databases
- Support of daily operations. Operational databases are important for daily business applications, like HR systems or CRM software.
- Real-time processing. They allow businesses to process data in real time. It may include completing sales transactions, updating inventory, or managing customer interactions.
- Consistency and integrity. They keep data consistent and unchanged, which is vital for reporting and decision-making.
- Integration. These databases often connect with other business systems to help data flow between departments and applications.
- Audit and compliance. They log transactions, which is essential for audit trails and regulatory needs.
History of operational databases
1960s: As computing developed, businesses needed systems that could manage daily operations in real time. The hierarchical and network database models were some of the first designs.
1970: E.F. Codd introduced the relational model for operational databases. It stored data in rows and columns, which was easy to manage and query.
Late 1970s and 1980s: Commercial relational database management systems (RDBMS) emerged. Oracle, IBM’s DB2, and Microsoft’s SQL Server became popular for operational database needs.
1990s: RDBMS like Oracle and SQL Server still dominated. However, the rise of the internet brought the need for scalable databases.
2000s: NoSQL databases like MongoDB addressed scalability needs. Big data solutions like Hadoop emerged, distinguishing between operational and analytical data.
2010s: Providers like AWS and Azure offered cloud databases. Hybrid systems blending transactional and analytical processing arose, as did distributed SQL databases. As technology advanced, open-source databases started rivaling commercial ones in features and reliability.