Skip to main content


Home Operational database

Operational database

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.