We have a wide range of tools, techniques, and frameworks to build a data warehouse. They will certainly improve the quality of output and speed wth which a warehouse is built. But key factor for successful data warehose design is how well you know the data you are dealing with.
ETL is an abbreviation for Extraction Transformation Loading. Purpose of ETL is to get data out of the source systems and load it into the data warehouse. Simply a process of copying data from one place to other. Typically, data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database.
Data mart is often interchangingly used to mean a data warehouse. If you are not proficient, you may be one of those who said data mart while it is supposed to be a data warehouse. This article will explain the concept of data mart and how it is different from a data warehouse.
OLAP stands for OnLine Analytical Processing and it deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. OLAP helps to answer multi-dimensional analytical (MDA) queries in a fast manner.
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