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ETL Channel

Extract, transform and load (ETL) are primary steps in reporting and in the creation and maintenance of a data warehouse. ETL is accomplished through the use of tools offered by different vendors that manage all three steps: data is extracted selectively from a chosen database, transformed into formats that support queries, and loaded into tables in the data warehouse or other target database. The high volumes of data processed via ETL make performance, time deadlines and cost primary issues and challenges for database administrators.


Murphy’s Laws of Data Warehousing: What Goes Bump in the Night?

Murphy’s Law tells us that whatever can go wrong will go wrong. This axiom applies to all aspects of life, including data warehousing.


Technology Components of a Scalable Architecture

All of the components of a business intelligence architecture must independently scale horizontally and/or vertically or through specific functionalities

Ask the Experts

With respect to ETL integration from one system to the other, what steps needs to be considered when planning an aquisition or merger?

What are your views on the advantages and/or disadvantages ETL tools and data modeling versus code?

How can I enhance the productivity of my fact table load?

How important is data reconciliation for an ETL application?

Please give some suggestions to prepare an estimation effort for our ETL process.

White Papers

Sunopsis Integration Suite: An Evaluation by Bloor Research

By Bloor Research

Third Generation ETL: Delivering the Best Performance

By Yves De Montcheuil and Chris Dupupet

Advanced ETL with Pentaho Data Integration

By By Christopher Lavigne

Evaluating Real-Time Data Integration Solutions

To V or Not To V: Business Intelligence Gets Virtual


Building and Managing the Meta Data Repository: A Full Life-Cycle Guide

By David Marco

Managing Gigabytes: Compressing and Indexing Documents and Images

By Ian H. Witten, Alistair Moffat, Timothy C. Bell

Industry Vendors