Data warehouse

Data Warehouse and their components

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 and decision making. In simple terms it is integrated time variant, consolidated, subject oriented non volatile system.
Subject oriented: Data is arranged in subject wise in datawarehousing because every department will have their own analytical patterns and also each department requires their departmental data only.
Integrated: since OLTP is application specific and database specific, So while reading and loading onto warehouse we need to covert data formats and also perform business calculations.
Non-Volatile: Once data is entered into warehouse it will not override unlike OLTP systems.
Time Variant: Data warehouse contains period with snapshot of data.
  • Data in the data warehousing is categorized in two  parts. 
  • One is the transactional data which is numerical in nature which helps to derive key business performance indicators/metrics. This kind of data is called as measures and the table which stores measures is called fact tables. 
  • The data  which explains the characteristics of measures is called dimensional data. 
  • A table which contains dimensional information is called dimension table.
  • So  data warehousing is a collection of various tables like dimension, facts etc..

Data Mart

Data mart is a subset of Data warehouse which will have the detailed data of the subject. Here the subject means any business related information. A data mart is a small data warehouse built to satisfy the needs of a particular department or business area. The data mart typically contains a subset of corporate data that is valuable toa specific business unit, department or set of users.
This subset consists of historical, summarized, and possibly detailed data captured from transaction processing systems.


ODS (Operational Data Store)

A subject oriented, integrated, volatile, current valued data store containing only corporate detailed data. It contains more responsive integrated real time data. It can be used to do analysis/mining on transactional level of data.
For example a banking ATM system to allow integration independent operational systems like loan savings etc..

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