Technics Publications

Cloud Data Warehousing Volume II: Implementing Data Warehouse, Lakehouse, Mesh, and Fabric

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Cloud Data Warehousing Volume II: Implementing Data Warehouse, Lakehouse, Mesh, and Fabric, by Barry Devlin

Cloud. Data. Warehousing. The mythos conjured by three simple words has taken marketing to new levels of hyperbole. Cloud data warehousing, together with AI (of course) will change all business decision making and action taking.


Chapter 1: Introducing Volume II

Cloud data warehousing: Volume I

Volume II overview


Chapter 2: Foundational architectural design patterns

Data Warehouse Classic ADP

Data Lake Classic ADP

Logical Data Warehouse ADP

Problems of the foundational ADPs


Chapter 3: From Information to Action via Meaning

The dilemma of the decision maker

The manifest meaning model, m3


Chapter 4: Diving into the data lakehouse

Data lakehouse as defined by its inventors

Generic foundational cloud ADP

Data Lakehouse ADP

Evolving the data lakehouse concept

Data lakehouse ADP—conclusions


Chapter 5: Unraveling the data fabric

Data Fabric as defined by Gartner and others

Data fabric—a conceptual image

Data fabric—technological foundations

Data fabric ADP

Data fabric ADP—conclusions


Chapter 6: Untangling the data mesh

Data mesh—problem and solution

Data mesh—principles and practices

Data mesh ADP

Data mesh ADP—conclusions


Chapter 7: Journey’s end

Gartner’s hype cycle for data management

Comparing the cloud-focused ADPs

In conclusion


Glossary of acronyms


In addition to the expected cloud data warehouse model, three new delivery patterns have been spawned: data lakehouse, fabric, and mesh. However, as seen in Volume I of this series, the conceptual and logical architectures change only minimally as we move from on premises to the cloud. So, why these very different solutions?

In Volume II, Dr. Barry Devlin—a founder of data warehousing—offers a framework of architectural design patterns (ADPs) to allow implementers to easily compare and contrast these different, proposed solutions. To evaluate their pros and cons according to a common model and in consistent terms. To choose an approach best suited to particular business needs and specific technical starting points.

And looking to the behemoth of AI bearing down upon us, Barry proposes a set of conceptual models that allow possible answers to the foundational questions: In a world of burgeoning data and information, how do we really make decisions and should we entrust them to AI built upon cloud data warehousing?

About Barry

Dr. Barry Devlin is among the foremost authorities on business insight and one of the founders of data warehousing in 1988. With over 40 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer, and author of “Data Warehouse—from Architecture to Implementation” and “Business unIntelligence—Insight and Innovation beyond Analytics and Big Data,” and numerous White Papers. As founder and principal of 9sight Consulting (, Barry develops new architectural models and provides international, strategic thought-leadership from Cornwall.


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