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What is a Data Warehouse?

Data Warehouse

A data warehouse (DWH) is constructed by integrating data from multiple heterogeneous sources. It supports analytical reporting, structured and/or ad-hoc queries for strategic decision making. By definition it is Integrated, Time variant, Non-volatile, Subject oriented system.

Integrated: As OLTP is application specific and database specific, while reading and loading the data on to the warehouse we need to covert data types and formats, along perform business calculations based on the agreed data model.

Subject oriented: Data is arranged in subject wise in data warehouse because every department will have their own analytical patterns and also each department requires their departmental data only.

Non-Volatile: Once data is entered into warehouse it will not override unlike OLTP systems. Of-course, data will be archived and saved as per the retention policy of the enterprise but the historic data will be available.

Time Variant: Data warehouse contains the period specific snapshot data.

Data in the data warehouse can be categorized in two  parts. One is the transnational data which is numerical in nature which helps to derive key business performance indicators (KPI’s)/metrics. This  is called as measures and the table which stores measures is called fact tables.
The second type of data explains the characteristics of the derived measures which is called as dimensional data. A table which contains dimensional information is called dimension table. So  data warehouse is a collection of various tables like dimension, facts etc..

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