SSIS includes the capability to connect to a wide variety of sources with functionality to make the extraction process scalable and flexible, including many ways to target and filter source data. Data extraction requirements are sometimes unique, and will require variations on the examples and design patterns presented here. For example, you may need to combine some of the incremental extraction approaches with dynamic connections and data lineage tracking. But even given the wide variety of requirements you may run into, SSIS provides the functionality to handle many extraction scenarios.
With the extraction component of ETL
Chapter 4: Dimension ETL with SSIS
Dimension ETL Overview
Arguably, when looking at the development time investment required for a data warehouse ETL solution, dimension processing takes the longest, and is the most complex component. You may have
Does this analyst requirement sound familiar: “I want to be able to look at the history of list price changes for a product to be able to understand the supply and demand curve”? In other words, what the business
The good news is that SSIS comes with out-of-the-box functionality to handle dimension scenarios just like this. Specifically, it comes with a data flow transformation called the Slowly Changing Dimension (SCD) Wizard. As you will see, there are advantages to using the wizard, as well as some limitations you must consider, and ways to work through those limitations to create a scalable dimension process in your ETL.
But before going any further with how SSIS can be applied to dimension table ETL, you need to step back and review the basics of dimension, both in design and the tracking of history.