The world of business intelligence is maturing and early years of turbulant time is over. This progress is witnessed with increased importance seen from companies to drive their BI initiative from a business point of view rather than technology driven. Additionally, we have seen steady progress in the development of standards, models, and frameworks in the last few years.
Rome wasn't built in a day, so does a business intelligence system of any organization. It requires lot of strategies and efforts to build such a solution.
To start with the effort of building business intelligence system, one needs a framework comprising of best practices, policies, and standards. Business intelligence architecture, by providing this framework, ensures that the development efforts of multiple projects fit neatly together as a cohesive whole to achieve desired BI system.
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.
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