Cloud Data Warehousing Volume II: Implementing Data Warehouse, Lakehouse, Mesh, and Fabric
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.
Topics
Chapter 1: Introducing Volume II
Cloud data warehousing: Volume I
Volume II overview
Takeaways
Chapter 2: Foundational architectural design patterns
Data Warehouse Classic ADP
Data Lake Classic ADP
Logical Data Warehouse ADP
Problems of the foundational ADPs
Takeaways
Chapter 3: From Information to Action via Meaning
The dilemma of the decision maker
The manifest meaning model, m3
Takeaways
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
Takeaways
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
Takeaways
Chapter 6: Untangling the data mesh
Data mesh—problem and solution
Data mesh—principles and practices
Data mesh ADP
Data mesh ADP—conclusions
Takeaways
Chapter 7: Journey’s end
Gartner’s hype cycle for data management
Comparing the cloud-focused ADPs
In conclusion
References
Glossary of acronyms
Index
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 (www.9sight.com), Barry develops new architectural models and provides international, strategic thought-leadership from Cornwall.
Bestsellers
Faculty may request complimentary digital desk copies
Please complete all fields.