I’ll be delivering an all-day deep dive into using SSIS for data warehouse ETL processes the day before SQLSaturday #89, on Friday, September 16th. We’ll be taking an in-depth tour of implementing data warehouse extract, transform, and load processes with SSIS, with plenty of demonstrations and sample code. If you’ve ever wondered about how to handle data errors during your ETL, how to handle updates to large fact tables, or how to load a dimension table that combines type 1, 2, and 3 attributes, then come to this pre-con. We’ll cover all of that, plus a lot more. We have a reduced rate on the pre-con until July 1st, so now’s a great time to register.
Pre-cons like this are some of the most cost effective training you can get – plenty of time to both cover a topic from end to end, and to dive into the real implementation details that are often missing from shorter presentations because of the time constraints. I hope to see you there!
Data Warehousing with SSIS Deep Dive
Want to learn more about implementing data warehouse ETL with SQL Server Integration Services? Attend this full day seminar, and we’ll cover using SSIS for data warehousing in-depth. You’ll learn everything you need to know to populate your data warehouse with data. We’ll cover how to develop a common framework for your packages, automate the creation of rote packages for staging data, implement common patterns for handling various types of dimensions and fact tables, and how to instrument your packages to identify and recover from failures when loading data. We’ll be using the AdventureWorks databases for the examples, so bring along a laptop configured with SQL Server 2005 or later, and the AdventureWorks sample databases installed. We’ll also cover how the upcoming Denali release of SQL Server affects what we discuss in this seminar.
- Laying out a framework for your ETL
- Restartability and Recoverability
- Handling Dimensions
- SCD Type 1
- SCD Type 2
- Advanced Dimension Types
- Handling Facts
- Periodic Snapshot
- Accumulating Snapshot
- Advanced Fact Patterns
- Handling Processing Errors
- Handling Data Errors
- Recovering from Errors
- Best Practices for Managing Your ETL