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DW Basics Channel

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Data warehousing is a foundational practice that supports enterprise reporting, business intelligence and decision support. Data warehouses and data marts are created across levels of sophistication and different philosophical approaches, but typically involve extracting and transforming data from operational/transactional databases and loading it to a repository for shared use and analysis. The DW Basics channel has been created to capture and aggregate stories related to fundamental approaches and practices for creating, managing, utilizing and maintaining a data warehouse.


Data Warehousing and Business Intelligence Strategies for Small and Medium Businesses

This article discusses some key strategies that SMBs could take advantage of to institute a simple business intelligence and data warehousing program

A Generalized Approach to the Creation of Bridge Records

Applied to data warehousing, a bridge table relates a fact record to one or more dimension records. Where a fact has a one-to-many relationship with a dimension, or many-to-many, you may choose to handle that with a bridge table

Real Time Data Warehousing, Part 3

This third and final article of the series addresses the questions “What causes the biggest headaches?” and “What can do you do about it?”

10 Mistakes to Avoid in a Business Intelligence Delivery

Every data warehousing and business intelligence project teaches us something. It is generally on failures, however, that we base our new success. You can learn from other’s failures, 10 of which are discussed in this article.

Real-Time Data Warehousing, Part 2

This article addresses the question “How is real time achieved?”


Back to the Basics of Data Warehousing

Approaches, methods and techniques from the early days of data warehousing are still applicable today

Getting Started on Your Enterprise Business Intelligence and Data Warehousing Journey

Here are a few more things to consider before getting started or going too far down the path of developing an enterprise business intelligence and data warehouse capability.

Drill Down to Ask Why, Part 2

In this month’s column, I’ll continue with this example and explore how to implement analytic capabilities to explore “why” in a real data warehouse/business intelligence system.

Analytical Database Options

Relational databases are ill-suited for analytical processing. Until recently, database developers were able to overcome the problem.

Building a Leadership Foundation

I’ll suggest areas where information management leaders can look to build a leadership foundation.

Ask the Experts

Can anyone share some experience as to how to test a data warehouse?

When is it better to have normalized data to create data marts and when is it better to have dimensional data?

How do I convince my reluctant boss to begin work on a data warehouse?

Where is the best place for a BI application to reside?

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

White Papers

Spend Data Warehouse on Steroids

By Zycus

An Architected Approach to Integrated Information

By David Waddington

KALIDO Business Modeling

By Bruce Ottmann

Data Warehouse Lifecycle Management

By Cliff Longman

Data Warehousing Ensuring Data Integrity

By Cindy Maurer


The Enterprise Data Warehouse: Planning, Building, and Implementation

By Eric Sperley

Enterprise One to One: Tools for Competing in the Interactive Age

By Don Peppers, Martha Rogers

Data Warehousing Advice for Managers

By Patricia L. Ferdinandi

Industry Vendors