In my last entry on this subject, I discussed some of the impediments to the smooth extraction of transactional data in the cloud for the purposes of analytical processing. Today, I’m discussing why automation of data extraction is the way to go.
Imagine a 3-layer stack made up of Source, Target, and the Connection between the two. In this example, we have a Source (the transactional data) that is less accessible than a standard DBMS. The accessibility issue is caused by
- the data not being in-house
- the data is an abstraction (rather than individual tables)
So instead of joining SQL Server Tables that make up your General Ledger system to find specific transactions, you now have to log into a cloud using a browser and look for Assets. Therefore, when you have to extract the Assets data from the source, you’ll have to find or write a query that creates the Assets data extract and you no longer have the ability to directly interact with the underlying tables, as they’ve been hidden from view.