Blog: Rick Sherman Subscribe to this blog's RSS feed!

Rick Sherman

Welcome! In addition to data integration, my BeyeNETWORK blog will include observations on the business and technology of performance management, business intelligence and data warehousing. Most posts will be hosted on my Data Doghouse blog, so feel free to leave comments here or on the Data Doghouse. If you'd like to suggest topics or ask me any questions, please email me at rsherman@athena-solutions.com.

About the author >

Rick has more than 20 years of business intelligence (BI), data warehousing (DW) and data integration experience. He is the founder of Athena IT Solutions, a Boston-based consulting firm that provides DW/BI consulting, training and vendor services; prior to that he was a director/practice leader at PricewaterhouseCoopers.  Sherman is a published author of more than 50 articles, an industry speaker and has been quoted in CFO and Business Week. He also teaches data warehousing at Northeastern University's graduate school of engineering. You can reach him at rsherman@athena-solutions.com and follow him on Twitter at https://twitter.com/rpsherman.

Editor's Note: More articles and resources are available on Rick's BeyeNETWORK Expert Channel. Be sure to visit today!


hand-stop.jpgThis is a continuation of an earlier post that discussed the problems of hand-coding using ETL tools.

What Went Wrong?

There
are two aspects of effectively leveraging an ETL tool. First is
learning the tool's mechanics. e.g. taking the tool vendors' training
either in a class or through their on-line tutorials. Most IT people
have no problem learning a tool's syntax.  Since they most likely
already know SQL, they learn the tool very quickly.

But the second aspect actually involves understanding ETL processes.
This includes knowing the data-integration processes needed to gather,
conform, cleanse and transform; understanding not only what is
dimensional modeling but why and how do you deploy it; being able to
implement slowly changing dimensions (SCD) and change data capture
(CDC); understanding the data demands of business intelligence; and
being able to implement error handling and conditional processing.

>>>continue reading this post on The Data Doghouse


Posted November 10, 2009 9:34 AM
Permalink | No Comments |

Leave a comment