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The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). [2] OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. [3]
An OLTP system is an accessible data processing system in today's enterprises. Some examples of OLTP systems include order entry, retail sales, and financial transaction systems. [5] Online transaction processing systems increasingly require support for transactions that span a network and may include more than one company.
Operational database management systems (also referred to as OLTP databases or online transaction processing databases), are used to update data in real-time. These types of databases allow users to do more than simply view archived data. Operational databases allow you to modify that data (add, change or delete data), doing it in real-time. [1]
An OLAP cube is a multi-dimensional array of data. [1] Online analytical processing (OLAP) [ 2 ] is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.
With greater data demands among businesses, [citation needed] OLAP also has evolved. To meet the needs of applications, both technologies are dependent on their own systems and distinct architectures. [7] [6] As a result of the complexity in the information architecture and infrastructure of both OLTP and OLAP systems, data analysis is delayed.
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Response time is an effective performance measure of OLAP systems. OLAP applications are widely used for data mining. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas). OLAP systems typically have a data latency of a few hours, while data mart latency is closer to one day.
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