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Data Engine Thinking

Data is evidence on past activity, events that are frozen in time for us to uncover and analyze. Without understanding the context of how it is created, data is simply 'stuff'.

Data Engine Thinking covers the end-to-end methodology to deliver a data solution that is truly designed to adapt to progressive understanding - and ultimately meet the business' needs.

  • Design and implement a solution that is designed for change
  • Solve real-world problems encountered when working with data
  • Fully automate your delivery

 

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Overview

What's inside the book

For many years, data solutions have been developed to make sense of data, and turn ‘stuff’ into actionable information that is fit for purpose. But, as the business changes, the methodology changes, and the understanding of the subject matter changes, these solutions often cannot keep up and become too complex, unwieldy.

A data engine understands and embraces this, it is a data solution that is truly designed for change. The data engine incorporates the critical data interpretation concepts, models, frameworks, and  approaches into an adaptable data solution that follows the business wherever it goes.

Data Engine Thinking - it is the complete approach for delivering a truly flexible, extensible and robust data solution. In this book, we follow FastChangeCo's journey from it's siloed and inflexible data platforms towards a solution that fully meets the business' requirements.

Each chapter starts with a practical explanation of how to design and implement a key component of the solution, and then follows FastChangeCo to experience what this means in the real world.

The world of data

Meet FastChangeCo

FastChangeCo hires Michael Mueller to deliver the best data solution that can possibly be designed.

Michael's first task is to understand what's 'wrong' with the data, the drivers for change, and the need for a new vision and strategy.

What does good look like?

Read more …

The data landscape

Data Quadrant Model

How can we meaningfully talk about data in our organization?

To define a suitable data vision and strategy, Michael introduces the Data Quadrant Model to FastChangeCo.

The goal is to align various perspectives on what it means to work with data.

Read more …

Architecture

Designing for change

The team sits down and discusses what it takes to deliver a data solution that is adaptable and robust. What layers, areas and concepts need to be in place?

Design patterns are introduced, and used to communicate the rationale behind the decisions made to all stakeholders.

Read more …

Implementation

Automating the delivery

Metadata management, code generation, and DevOps are introduced by Jeff Jones, who brings invaluable experience to the table.

Combined with the implementation patterns created by Amal Leyla Qasim, the complete solution can now be delivered. 

Read more …

Q

"This book is a monumental achievement, a classic even before release. I predict this will be as much a landmark of progress and contemporary standards for data warehousing as the Kimball series were 20+ years ago. If you are in the business of storing large volumes of data, and need to integrate sources, this book is for you. If you want to make sense of temporal logic in data, this book is for you, too. Great technical insights, but arguably at least as important are the lessons from the trenches on why some DWH projects succeed, and so many of them struggle. Highest praise I can give, arguably most valuable book on Business Intelligence I ever read."

- Tom Breur -

 

 

 

Roelant Voss & Dirk Lerner

About the Authors

Roelant Vos & Dirk Lerner

Having worked as a consultant, trainer, software vendor, and decision maker in the corporate world over the years, Roelant has observed data management from many different points of view.

Throughout his career, the common theme has always been a passion for automation & code generation, patterns, and model-driven design in general. Roelant has always felt that this is the key to making data solutions easier, more manageable and truly flexible. 

Dirk is an experienced independent consultant and managing director of TEDAMOH. He is considered a global expert on BI architectures, data modeling and temporal data. Dirk advocates flexible, lean, and easily extendable data warehouse architectures.

As a pioneer for Data Vault and FCO-IM in Germany Dirk wrote various publications, is a highly acclaimed international speaker at conferences and author of the TEDAMOH blog.

 

 

Q

“With the launch of this excellent book, the data warehousing industry will move another step closer to achieving success. Dirk and Roelant, two brilliant thinkers and diligent practitioners, are presenting a vision of a realistic and complete roadmap for an agile business data solution. Any and all data warehouse practitioners, from business analysts to modelers to developers, will benefit greatly from the insights and guidance provided in this book.”

Hans Hultgren, President, Genesee Academy, LLC

 

 

 

 

 

Data Engine Thinking

Order

The book Data Engine Thinking is expected to be published in the first half-year of 2025.

It is also possible to register to receive a notification when the book is available. When the book is published, you can order your copy of Data Engine Thinking via the links below.

The prices shown below are examples. They are not yet the final prices.

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Limited Edition

129 € *

Get both the Kindle and
printed version, with additional
Data Engine Thinking merchandise!

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Print

119 € *

Order your printed copy
of Data Engine Thinking.

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eBook

99 € *

Obtain Data Engine Thinking as
a Kindle version for your e-reader.

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* Prices not yet final. Subject to change without notice! 

 

 

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Data Engine Thinking

Examples

Technologies change at a rapid pace, and what is a good example today may not be the best way to solve specific challenges tomorrow.

Data Engine Thinking advocates a separation between the concepts and implementation - design patterns and solution patterns. Concepts change too, but at a much slower rate than the technology to implement these concepts does.

To provide meaningful examples and up-to-date content on some of the sections in the book, we use Github to manage sample code. Throughout the book, we refer to the Data Engine Thinking Github repository.

Visit our Github

 

Frequently asked questions

 

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Data Engine Thinking - Training

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