ETL vs. ELT in a Parallel Data Warehouse
Traditionally we have been doing ETL; extract of the data from the source systems, then transforming it to fit our needs, and finally loading them into the end target such as a fact table.
However, in the world of Massive Parallel Processing (MPP) scale out data warehousing, can this approach still be used?
Some advocate the ELT approach instead, which extract the source data, then load it into the end target, and finally do the transformations at the data in the target. But what are the benefits of this? And does it really matter?
This session tries to answer these questions by looking at the difference between the ETL and ELT approach, and how SSIS can be used with a Parallel Data Warehouse.
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David Peter Hansen is a Microsoft Certified Master (MCM) and has over a decade of experience with database development and administration on SQL Server and the Microsoft Business Intelligence platform. Today, he works as ICT Database Developer at United Nations Office for Project Services (UNOPS). He specializes in developer coaching as well as scalable architecture and performance tuning on large-scale data warehouses and BI solutions. He is a frequent speaker at conferences, and have given talks in Europe, USA, and South Africa.
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