BIAnalytix Tip Summary: The Plumbing behind the Magic

In the custom designed, highly modernized kitchen, we do not often think of the path that water takes before it comes out of the faucet. We flip up the handle, water gushes out as expected. Only when something goes horribly wrong, and the water is up to our knees in the basement, do we ever even consider all of the pipes, valves, junctions, and solder that make the magic of running water on demand possible. So too with the corporate data warehouse; when slicing and dicing data at the speed of clicks in a pivot table one doesn’t often consider all of the heavy lifting that must occur to make the instantaneous viewing of multiple aggregated facts, across multiple dimensions, over years of history, possible. The nightly ETL process is the plumber in the data warehouse, and data movement is his game.

ETL (Extract Transform Load) is the process by which data is pulled from the various source systems, massaged to fit the shape of the data warehouse structure, and pushed into the data base that underlies the cubes. There are many products on the market that can perform ETL, one can even “roll your own” ETL package with just SQL script. The key takeaway with talking about ETL is that, whichever product or strategy one embraces, the ETL process should be robust, auditable, and fast.

BIAnalytix is a media data warehouse and business intelligence system from Decentrix which enforces strict and formal standards for data integrity and correctness. For example:
  • The MSTAR™ Star Schema has all relationships enforced, meaning that every spot has to have a valid advertiser, product category, etc.
  • 100's of Audit control totals are validated from the source systems to the final data warehouse ensuring that the data loaded into the warehouse is precisely balanced with the source operational systems
  • External Reconciliation processes compare report totals and details in both the source system and the Data Warehouse
  • Media Data Management (MDM) is supported within the Data Warehouse with Master Relationships, Mapping relationships, and Corporate Mapping

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