Kimball University: Three ETL Compromises to Avoid

Why neglecting slowly changing dimensions, failing to capture metadata and overlooking scope creep can be the undoing of a dimensional data warehousing initiative.

Slowly Changing Dimensions

Data Warehousing 2.0 and SQL Server: Architecture and Vision

Architecture and data warehousing are not static. From the first notion of a data warehouse to a full-blown analytical processing architecture that includes data marts, ETL, near line storage, exploration warehouses, and other constructs, data warehousing and its associated architecture continue to evolve. In 2008, the book on the latest evolution of data warehousing appeared – DW 2.0: The Architecture for the Next Generation of Data Warehousing (Morgan Kaufman). In that book the general architecture for data warehousing in its highest evolved form appeared.

What a Data Warehouse is Not

Recently, I was at a conference, and I heard the following discussion about what a data warehouse was. One person suggested that a data warehouse was really all the old legacy systems connected by software that could access the data. By calling such a contraption a data warehouse, the organization could avoid having to do the hard and complex work of integration. There are so many problems with this federated approach to a data warehouse that they are almost not worth repeating here. But (once again!) here goes.

Kimball University: Six Key Decisions for ETL Architectures

Best-practice advice on software vs. coding, where to integrate, how to capture changed data, when to stage data, where to correct data and what latency levels to shoot for.

Energy Intelligence

Given that companies spend on average between 7 and 12 percent of their annual budgets on energy – a focus on reducing energy consumption can have significant bottom-line impact

How to Calculate Data Warehouse Reliability

This article outlines a more objective and analytical approach to the ETL and data flow architecture selection based on a set of variables with an objective to enhance the reliability of the overall data warehousing solution.

Ten Pitfalls In Establishing An Enterprise Architecture

Avoiding getting snared in these traps avoids having to spend a lot of money later to fix problems, Gartner says. In addition, EA benefits can be realized faster and the risk of program failure is reduced.

Implementing a SQL Server Fast Track Data Warehouse

This paper defines a reference configuration model (known as SQL Server Fast Track Data Warehouse) and a CPU core-balanced approach to implementing a symmetric multiprocessor (SMP)-based SQL Server data warehouse with proven performance and scalability expectations for sequential I/O data workloads.

Kimball University: Five Alternatives for Better Employee Dimension Modeling

The employee dimension presents one of the trickier challenges in data warehouse modeling. These five approaches ease the complication of designing and maintaining a 'Reports To' hierarchy for ever-changing reporting relationships and organizational structures.