WROX.PROFESSIONAL.MICROSOFT.SQL.SERVER.2014.INTEGRATION.SERVICES.2014 by Mike Davis & Chris Rock
Author:Mike Davis & Chris Rock
Format: epub
Published: 0101-01-01T00:00:00+00:00
What benefit did you gain? Well, because you did this conversion in the source extraction query, SSIS receives the data in a cleaner state than it was originally. Of course, there are bound to be other data quality issues that SSIS will need to deal with, but at least you can get the trivial ones out of the way while also improving basic performance. As far as SSIS is concerned, when it sets up the pipeline column structure, it will use the names and types represented by the query. For instance, it will believe the IsActive column is (and always has been) a BIT — it doesn’t waste any time or space treating it as a 17-byte DECIMAL. When you execute the package, the data is transformed inside the SQL engine, and SSIS consumes it in the normal manner (albeit more efficiently because it is cleaner and sharper).
You also gave the columns friendlier names that your ETL developers may find more intuitive. This doesn’t add to the performance, but it costs little and makes your packages easier to understand and maintain. If you are planning to use the data in a data warehouse and eventually in an Analysis Services cube, these friendly names will make your life much easier in your cube development.
The results of these queries running in a Data Flow in SSIS are very telling. The old query returns over 19,000 rows, and it took about 0.3 seconds on the test machine. The new query returned only a few dozen rows and took less than half the time of the old query. Imagine this was millions of rows or even billions of rows; the time savings would be quite significant. So query tuning should always be performed when developing SSIS Data Flows.
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