OLAP is an important arm of Business Intelligence, with relational databases, report writing and data mining, under its umbrella.
The terminology OLAP was created as a little alteration to the traditional term ‘Online transaction processing (OLTP)’.
OLAP encompasses competencies like unlimited report viewing, complicated analytical calculations and budget / predictive planning.
It’s a data processing method providing multi-dimensional view of the measures and enables user easy and speedy access of consolidated enterprise data.
OLAP cube consists of numeric facts called measures which are categorized by dimensions. The cube metadata may be created from a star schema or snowflake schema of tables in a relational database.
OLAP works as a fundamental stone to many types of business apps pertaining to
- Business Performance Management
- Planning, Forecasting, Budgeting
- Financial Reporting
- Simulation Models
- Knowledge Discovery
- Data Warehouse Reporting
Features
- Cube Design
- MOLAP, ROLAP (Multidimensional and Relational OLAP)
- MDX Queries
- Slice and Dice
- Drill down
- Ad-hoc Analysis
Major Advantages
- In-depth analysis because of multiple dimensions
- Empowers complicated analysis of bulky data volumes as against intricate business-queries
Key OLAP Technologies
Pentaho BI, Mondrian, Apache Druid, Click House, Pinot, Apache Kylin, etc.
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