An analytic database, also called an analytical database, is a read-only system that stores historical data on business metrics such as sales performance and inventory levels. Business analysts, corporate executives and other workers can run queries and reports against an analytic database. The information is updated on a regular basis to incorporate recent transaction data from an organization’s operational systems.
An analytic database is specifically designed to support business intelligence (BI) and analytic applications, typically as part of a data warehouse or data mart. This differentiates it from an operational, transactional or OLTP database, which is used for transaction processing – i.e., order entry and other “run the business” applications. Databases that do transaction processing can also be used to support data warehouses and BI applications, but analytic database vendors claim that their products offer performance and scalability advantages over conventional relational database software.
There currently are five main types of analytic databases on the market:
Columnar databases, which organize data by columns instead of rows – thus reducing the number of data elements that typically have to be read by the database engine while processing queries.
Data warehouse appliances, which combine the database with hardware and BI tools in an integrated platform that’s tuned for analytical workloads and designed to be easy to install and operate.
In-memory databases, which load the source data into system memory in a compressed, non-relational format in an attempt to streamline the work involved in processing queries.
Massively parallel processing (MPP) databases, which spread data across a cluster of servers, enabling the systems to share the query processing workload.
Online analytical processing (OLAP) databases, which store multidimensional “cubes” of aggregated data for analyzing information based on multiple data attributes.
'analytic database' is part of the:
View All Definitions
Dig deeper on Business intelligence best practices