The Thinking Person’s Guide to Data Warehouse Design

Data Warehousing and Business Intelligence
Location: Ballroom C Level: Intermediate
Average rating: ****.
(4.38, 8 ratings)

Poor database design is the number one cause of both database downtime and bad performance, and is especially an issue with large data warehouses. This session will teach you how to navigate down through the process of building an analytic database design that will hold up under pressure. Topics covered include:

Building the logical design:
-Moving from transactional to analytic schemas
-Stars, snowflakes, and more
-Vertical and horizontal partitioning model trade-off’s
-Quick note on using MySQL Workbench to design data warehouses

Transitioning to the physical design:
-How to decide on the right storage engine(s)
-When to/not to use row and column-oriented storage engines
-How to decide on a partitioning strategy
-Optimizing for fast data loads
-The love-hate relationship with indexes
-Hardware architectures: SMP, MPP, or both?
-Use case benchmarks for the above designs

Monitoring and tuning the design:
-Monitoring checklist (database, OS, and storage)
-SQL diagnostic troubleshooting best practices
-When to quit and start over

Robin Schumacher


Robin Schumacher is VP of Products at Calpont and has over 20 years of database experience in Teradata, MySQL, Oracle, SQL Server, and Sybase. He is the author of three database performance tuning books, has written many articles on data warehousing and database tuning, and previously served as the database software reviewer for Intelligent Enterprise and Data Management Review magazines. Before joining Calpont, Robin was Director of Product Management for MySQL/Sun.

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Picture of Sheeri K. Cabral
Sheeri K. Cabral
04/19/2010 1:48pm PDT

Slides are online at

Picture of Sheeri K. Cabral
Sheeri K. Cabral
04/15/2010 11:23pm PDT

The video of this presentation is at:

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