Data Distribution - Part 1

The start of a new series from Leo Peysakhovich that looks at some of the issues with moving data around between systems and ensuring that it is in sync between them.

Building a Data Warehouse Blueprint for Success

One of the most integral components and critical success factors of any enterprise data warehousing initiative is the Solutions Architecture document, a high-level conceptual model of a data warehousing solution. Learn why this collaborative effort that addresses the needs of all major stakeholders, including both the business units and Information Technology (IT), is essential.

Data Warehousing Architecture - Designing the Data Staging Area

The staging area tends to be one of the more overlooked components of a data warehouse architecture, and yet it is an integral part of the ETL component design. Learn why it is best to design the staging layer right the first time, enabling support of various ETL processes and related methodology, recoverability and scalability.

How to Conduct Effective Data Warehouse and Business Intelligence Software Evaluations

Denise Rogers discusses the essential tasks in conducting effective software evaluations revolving around data warehousing and business intellegence. Each step has a dependency on the previous one, starting with establishing the framework of the evaluation and adding progressively elaborate data that facilitates a decision making process that is resolute.

HASHBYTES can help quickly load a Data Warehouse

Data warehouse loads can be time consuming - this method can be used in some instances to help speed things up.

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.