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Thursday 8 March 2012


The constant growth of Data Analysis and Business Intelligence Applications, understanding OLAP and OLTP having the important Place.

The design of Data Warehouse and Online Analytical Processing (OLAP) cubes is basically different than Online Transactional Processing (OLTP). 

Source of Data: OLTP is original Source of data that maintains the Day by Day transactions and processes for every Business.
Source of Data: The source of data of OLAP is OLTP, Consolidated from various OLTP Sources.

Purpose of Data: To run and control the fundamental business task and record all the events inside.

OLTP Reveals the copy of the ongoing Business processes.

The Main aim of this data is to help reporting, Decision support, Data analyzing and for problem Solving.

This data reveals the Multi-Dimensional views of various kinds of business Activities.

Insertion and Updation is very fast in OLTP and this operation is initiated by End Users. All the OLTP is Normalized one and Doesn't having any duplicate records in any situation.
Periodically Its long process to insert the data and update the data But retrieving the data from OLAP is easy.

In OLTP, the Query Processing is normally standardized and simple query that gives the result of few records.
Typical the query processing in OLAP is long and tedious and often with complex queries involving aggregations.
The processing speed is very speed and the size of the OLTP Size (MB to GB) is very small compare with OLTP if historical data is archived.

The processing speed in OLTP depends upon the size of the data involved, that may batch processes and complex queries may take hours to one and query speed can be improved by creating Views and Indexes.

The size of OLAP is very Large (GB to TB) and It contains the consolidated data of OLTP with more aggregation structures and Historical data.
Database design of OLTP is highly Normalized with many tables.
The database design of OLAP is in most cases de-normalized with the fewer tables and using Star or Snowflake schema.

1 comment:

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