in-database analytics

In-database analytics is a scheme for processing data within the database, avoiding the data movement that slows response time.  

In-database analytics is a technology that allows data processing to be conducted within the database by building analytic logic into the database itself. Doing so eliminates the time and effort required to transform data and move it back and forth between a database and a separate analytics application.

An in-database analytics system consists of an enterprise data warehouse (EDW) built on an analytic database platform. Such platforms provide parallel processing, partitioning, scalability and optimization features geared toward analytic functionality. 

In-database analytics allows analytical data marts to be consolidated in the enterprise data warehouse. Data retrieval and analysis are much faster and corporate information is more secure because it doesn’t leave the EDW. This approach is useful for helping companies make better predictions about future business risks and opportunities, identify trends, and spot anomalies to make informed decisions more efficiently and affordably.

Companies use in-database analytics for applications requiring intensive processing – for example, fraud detection, credit scoring, risk management, trend and pattern recognition, and balanced scorecard analysis. In-database analytics also facilitates ad hoc analysis, allowing business users to create reports that do not already exist or drill deeper into a static report to get details about accounts, transactions, or records.

 

See also: predictive analytics, association rules, data mining, business analytics, MapReduce

This was first published in January 2011

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