What is a Data warehouse?

Paths can be many but Destination is One. With this I mean to say, Different people have different explanations for a Data warehouse. But all the explanations and definitions aims to single destination i.e. Data storage. If you are thinking of aligning your career in BI/DW (Business Intelligence/Data warehousing), than you definitely need to know two names. I call these two persons as “Gods of BIDW”.

First name is Ralph Kimball and second name is Bill Inmon. You must be wondering why we need to know about these two personalities. This is because they have given some important Data warehouse concepts which need to be followed.

  • Ralph Kimball provided a “Bottom – Up approach”, whereas
  • Bill Inmon provided “Top – Down approach”.

In the world of BIDW, the most popular definition came from Bill Inmon, who said “A Data warehouse is a Subject-oriented, Integrated, Time-variant and Non-volatile collection of Data in support of Management’s decision making process”.

What makes a Data warehouse?

  1. Subject-Oriented : It means a Data warehouse can be used to analyze a specific subject area. For example – In an Organization, Subject area can be HR, Finance, Sales, etc. So, it can be used to analyze particularly HR or Sales.
  2. Integrated : With Integrated we mean data from heterogeneous data sources is integrated in Data warehouse. For example – Source A stores date in different format and source B stores date in other format, but in a Data warehouse, dates will be stored in a single format only.
  3. Time-Variant : Data warehouse only deals with historical data. It acts as a pit where Transactional data is dumped every day. We can retrieve data which is 6 months, 12 months old or even more than this from a data warehouse. For example – You hold a SBI bank account and updated your contact details. So, Transaction system can only provide you the current contact details whereas Data warehouse holds all the contact details associated with your account (Current and Previous both).
  4. Non-volatile : As explained above, Data warehouse is freakily mad about historical data. Once data comes in the data warehouse, it will not alter. Hence, historical data in a data warehouse is Non-volatile.

Another concept came from Ralph Kimball who explained Data warehouse in his own way and said “A data warehouse is a copy of transactional data specifically structured for query and analysis”.

This is a more like a functional view of a Data warehouse. Bill Inmon precisely stated how the Data warehouse is built which was lacking in Ralph Kimball’s definition.

Data warehouse usages

  • Trend analyzing for an organization.
  • Startegic planning.
  • Product forecasting.
  • Designing business models.
  • Preparing dashboards and reports with reporting tools.

Summary In this post, we have learnt

  • What do we mean by Data warehouse?
  • Definition provided by Bill Inmon.
  • Definition provided by Ralph Kimball.
  • How Transactional database is different from Data warehouse.
  • Data warehouse usages.

I hope you liked my article on “What is a Data warehouse?”. If you have any queries or feedback, kindly post them below in the comment section.
In Association with Amazon.in

VN:F [1.9.22_1171]
Your Feedback hels to Improve
Rating: 10.0/10 (23 votes cast)
VN:F [1.9.22_1171]
Rating: +16 (from 16 votes)
What is a Data warehouse?, 10.0 out of 10 based on 23 ratings
  • Hi chander,

    This is really great article , its very easy to understand the concept of datawarehouse .

    great one bro . Looking for more stuff from you.

  • Aanchal Kapoor

    Hi Chander,
    I always love your posts… The way you write is Superbbbbbb !!!!!! 😉

  • Selvakumar

    Good post bro:) Keep it up:) waiting for your next post in Data warehouse in Detail level.

    • Hi Selva,
      Thanks for the appreciation. Sure bro.

      Chander Sharma

  • Pandiyan

    Than q chander.

    • Hi Pandiyan,
      You are welcome bro !!! I am glad you searched this post and found solution to ur query?
      Keep reading n Sharing.

      Thanks,
      Chander Sharma

      • pandiyan

        thankyou chander sharma

  • Krishna Reddy

    Hi Chander Sharma Garu..

    This Is An Good Article to Understand Easily. We Are Waiting More Concepts From PhpRing….

    Thank you…….

    • Hello Krishna Reddy,
      Sure man !!! We are planning to write more on ETL tools and Data warehousing concepts. Stick to PhpRing and thanks a ton for the positive feedback.

      Thanks,
      Chander Sharma

  • vijay raj gupta

    Great post waiting for more..