Oracle Data Warrior

White Papers

These are some of the papers I have written for various user groups and technical publications. I expect to add more over time so check back often.

They are yours to download for reference (for FREE). Just please respect the copyrights. If you would like to use some of the content please send me a note first and annotate appropriately.

Data Vault Related Papers

Introduction to Data Vault Modeling

The purpose of this article is to present an introduction to the technical components of the Data Vault Data Model.  The examples provide a strong foundation for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to implement a Data Vault model for integration purposes whether it be an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store.

Using Decode for Change Data Capture

Do you have a need to automate an effective change data capture process in an Oracle database but you have non-Oracle source systems? Do your operational systems have no auditing or logging at all making change detection virtually impossible (without a bit by bit comparison)? Is your budget too small to afford third party tools or advanced Oracle options (or your staff is too inexperienced to implement them)? If so, this paper is for you. In implementing a Data Vault data model for the enterprise data warehouse at Denver Public Schools we learned how easy it was to do change data capture (CDC) against any dataset using good ol’ DECODE in a view. This paper will show you the actual SQL code to use and explain how it works to detect changes in that data when compared to a data warehouse table. In addition I will show and explain the views we use to determine when completely new records appears in the source as well.

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“Classic” White Papers

These are some older papers I wrote or co-authored. Some of the content has stood the test of time and still applies today. Others refer to specific versions of Oracle tools that may not be around anymore, but the concepts still apply.

Note: The contact information on these is definately outdated!

Agile Methods and Data Warehousing

Most people will agree that data warehousing and business intelligence projects take too long to deliver tangible results. Often by the time a solution is in place, the business needs have changed. With all the talk about Agile development methods and Extreme Programming, the question arises as to how these approaches can be used to deliver data warehouse and business intelligence projects faster. This paper will attempt to look at some of the principles behind the Agile Manifesto and see how they might be applied in the context of a traditional data warehouse project. We will also examine a new approach called the Data Vault to see if that methodology helps. The goal is to determine a method or methods to get a more rapid (2-4 week) delivery of portions of an enterprise data warehouse architecture.

A Case Study in Data Warehousing (2003)

So you want to build a data warehouse.  What are all the Oracle pieces of a data warehouse environment and how do they work together?  As with any large LEGO project, a data warehouse must be built on an architected infrastructure a.k.a. the big green thingy.  This presentation will explore our organization’s approach to building the Oracle infrastructure to support our data warehouse environment.   The various repositories that are needed, the specific Oracle tools used in developing and managing each process and step, what activities are managed by specific tool processes, software version compatibility issues, and interoperability issues will be covered in this presentation.

Jump-Start Your Data Modeling Effort (1997)

In general, one third of a data model (corporate or logical) consists of common constructs that are applicable to most organizations and the other two thirds of the model are either industry or enterprise specific. This means that most data modeling efforts are at some point re-creating data modeling constructs that have already been built many times before in other organizations. Doesn’t it make sense then to have a source from which you can get a head start on your data model so you are not “re-inventing the wheel” each time you are asked to develop a new system?

This paper will illustrate some examples of common or “Universal Data Models” related to one subject area and explain how they can be used as a starting point for most data modeling efforts. By using these constructs, both time and money can be saved in systems development efforts.