DWH - Data Warehouse

Now that we have an overview of the BI Components, it is time to drill into the first and the most important among them -> The Data Warehouse(DWH).

What is a Data Warehouse, anyways?

To keep it simple, let us take Bill Immon’s definition of the Data Warehouse (one of earliest authors on DWH):

“A Data Warehouse is a central repository for the Entire Enterprise”.

As we know, the end product of Business Intelligence are the reports/dashboards that assist the stakeholders in analyzing the trends and help them take decisions. But it is important that these reports are projected on data that is accumulated over a period of time(historical data) and not just recent data or data from a single system. In other words, these reports would make sense only when they are created on top of integrated and historical data. Considering the fact that Organizations these days have their data scattered across multiple systems, platforms and formats, DWH’s role becomes all the more crucial as it acts as a one stop destination.

How is it different from a traditional database?

Following are the differences between Operational/Transactional systems and a DWH:

Operational/Transactional database	Data Warehouse
It deals with operational data i.e. data involved in the operation of a particular system and it is characterized by a large number of short on-line transactions   Features §  Designed Primarily for fast INSERTs, UPDATEs, and DELETEs. §  Quick Singular Transactions. §  Operational/transactional Data §  Application specific DBs	It is a collection of Historical and integrated Data accumulated from various operational systems.    Features §  Designed Primarily for : SELECTs §  Fast Reading of Large Data Sets §  Historical Data or Archival Data §  Integrated data set with a global relevance

In my next post, we shall learn more on concepts and components associated with DWH.