Data Warehouse Concepts

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Data Warehouse Concepts


  • Online Transaction Processing.
  • Its fully transactional based
  • Transaction should be made as soon as possible
  • It’s a client server model
  • It handles with current data.
  • The SQL statements retrieves only small amount of data.
  • Data’s are in the form on “Normalized data”
  • Mostly used SQL statements are INSERT,UPDATE,DELETE
  • Heterogeneous data
  • Examples: Online Reservation System, Withdraw money from ATM and etc.,


  • Online Analytical Processing
  • Otherwise called as “Decision Support System”
  • Deformalized data
  • Mainly focuses on database
  • Its fully analysis and report oriented
  • Main users are Management and business people
  • It handles Historical Data
  • Only Bulk Load
  • Homogeneous data

Difference between OLTP and OLAP

                          OLTP                        OLAP 
Online Transaction Processing Online Analytical Processing
OLTPs are the original source of the data. OLAP data comes from the various OLTP Databases
It is transaction oriented. It is Analytical and report oriented.
Normalized data. De normalized data.
Handles current data Handles  historical data
Operations like insert, update, delete can be performed. Since it is mainly used for bulk loading of data we mainly perform the insert operation.Update and delete operations are performed in some cases where it is necessary.
Select query is used to retrieve only minimal amount of data. Select query is used to retrieve bulk volume of data.
Used by large number of end users. Used by minimal number of users.
Consist of heterogeneous data. Consist of homogenous data.

Tasks Performed When data coming from Live to data warehouse

  • DE normalizing the data.
  • Data cleansing.
  • Data trimming
  • Removing duplicate records.
  • Decoding the data.
  • Calculating the data.
  • Data validation if required
  • Null Value replacement.
  • Data type conversion.

Life Cycle of Data Warehousing Project:

  • Project Planning
    • Budget Allocation.
    • Internal resource allocation
    • BRS (Business Requirement Study) which involves
    • Dimensional modeling
      • This involves conversion of OLTP tables to OLAP tables.
      • This is performed by Dimension modeler.
      • Physical Design
      • Application Development
      • Deployment and training
      • Maintenance

Teams involved in Data warehousing:

  • Data Dimension modeler.
  • DBA
  • Report Developer
  • Report Admin
  • ETL developer
  • ETL admin
  • System Admin.

Dataware House

  • Managing Historical data and helps the management to take right decisions at right time
  • A DW is Subject oriented, nonvolatile and integrated collection of data to support management

Data mart

  • Data mart is a subset of data warehouse

DWH Design Approach

  • Top Down Approach

  • Bottom Up  Approach

Dimension Table

  • De normalized data
  • Must have one primary key
  • Contains less number of records than fact table
  • It contains large number of columns

Fact Table

  • Contains foreign key to all the dimension table
  • Contains large number of data than the dimension table

Dimension model type

  • Star Schema
  • Snow flake schema

Star Schema

  • Fact table is surrounded by many dimension table


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