|By Tad Anderson||
|December 13, 2011 08:00 AM EST||
|Data integration is a complex, detailed, sometimes excruciating boring activity, which is not an activity for the light at heart.
This book does a great job of digging into the details of the data transformations. It is not just a high level look at data integration, it gets into the weeds.
The book is broken down into three parts. I have listed them and the chapters they contain below.
Part 1 Overview of Data Integration - Types of Data Integration, An Architecture for Data Integration, A Design Technique: Data Integration Modeling, and Case Study: Customer Loan Data Warehouse Project.
Part 2 The Data Integration Systems Development Life Cycle - Data Integration Analysis , Data Integration Analysis Case Study, Data Integration Logical Design, Data Integration Logical Design Case Study, Data Integration Physical Design, Data Integration Physical Design Case Study, Data Integration Development Cycle, and Data Integration Development Cycle Case Study.
Part 3 Data Integration with Other Information Management Disciplines - Data Integration and Data Governance, Metadata, and Data Quality.
It also includes four appendices - Exercise Answers, Data Integration Guiding Principles, Glossary, Case Study Models.
The thing I like most about this book is the way the authors follow each new topic chapter with a case study chapter. They introduce the theory and best practices and then they bring that to life through a real world case study.
Another thing I like about the book is that it is down to earth and very realistic. I have recently been on a MDM project. There are a lot misleading resources out there. The same can be said for data integration. The theme of the misleading resources is, "Buy this tool, install it, and push the magic button, and watch the fairy dust do its magic." Tools are important, but they are only a small piece of the puzzle. Without the proper process and governance in place the tools are worthless. This book does a great job of covering the importance of data governance.
Another key is upper management support. Projects like these and MDM don't get done without upper management backing. Thinking that you can build it and they will come is just a path to self destruction.
The book also acknowledges and addresses the key difficulties with data integration projects. One I always dread is reverse engineering source systems. They are never documented and if they are the documentation is out of date. You must go from person to person and suck the information out of their heads.
Subject matters experts not only need to be identified, but they need to be given the time to help the project. This is always a problem, but with out upper management's support, it becomes impossible. The experts are usually the only source for all the maintenance issues going on.
The book has a ton of diagrams and tables that help with understanding the topic at hand. The authors do a great job of using them to visually get their points across. The book is also loaded with great real world example diagrams and data represented in tables that make the process they are covering easier to understand. It's like getting to go through a data integration project.
All in all I highly recommend this book to anyone in IT. Data integration is part of all IT projects. Managers, architects, DBAs, CIOs, developers, testers, and business users would all benefit from reading this book.
Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture
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