As I have mentioned in prior blog posts, I have been writing a data architecture book, which I started last November. The title of the book is “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh” and it is being published by O’Reilly.

All 16 chapters are now available in the O’Reilly Early Release program. It’s 258 printed pages. Check it out here. The Early Release program is a great way to start reading the book without having to wait until the entire book is done. Note you have to have an O’Reilly subscription to access it, or start a free 10-day trial, which gives you access to not only my book, but all the books published by O’Reilly and multiple other publishers.

My book now goes off to “production” where it will be thoroughly grammar checked, get a TOC and index, and have all the pictures redrawn. That process usually takes 2-3 months. The fully finished book should be available for download or for a printed copy by the end of January. The new cover below features the Discus fish (O’Reilly is known for having different animals on their cover).

Please send me any feedback on the book to jamesserra3@gmail.com. Would love to hear what you think!

Here is the likely final TOC:

1. Big Data
   • What is Big Data and how can it help you?
   • Data maturity
   • Self-Service Business Intelligence
   • Summary
2. Types of Data Architectures
   • Evolution of data architectures
   • Relational Data Warehouse
   • Data Lake
   • Modern Data Warehouse
   • Data Fabric
   • Data Lakehouse
   • Data Mesh
   • Summary
3. The Architecture Design Session
   • What is an ADS?
   • Why hold an ADS?
   • Before the ADS
   • Conducting the ADS
   • After the ADS
   • Tips
   • Summary
4. The Relational Data Warehouse
   • What is a relational data warehouse?
   • The top-down approach
   • Why use a relational data warehouse
   • Drawbacks to using a relational data warehouse
   • Populating a data warehouse
   • The death of the relational data warehouse has been greatly exaggerated
   • Summary
5. Data Lake
   • What is a data lake?
   • Why use a data lake?
   • Bottoms-up approach
   • Best practices for data lake design
   • Multiple data lakes
   • Summary
6. Data Storage Solutions and Process
   • Data storage solutions
   • Data processes
   • Summary
7. Approaches to Design
   • Online transaction processing (OLTP) versus online analytical processing (OLAP)
   • Operational and analytical data
   • Symmetric multiprocessing (SMP) and massively parallel processing (MPP)
   • Lambda architecture
   • Kappa architecture
   • Polyglot persistence and polyglot data stores
   • Summary
8. Approaches to Data Modeling
   • Relational modeling
   • Dimensional Modeling
   • Common Data Model (CDM)
   • Data Vault
   • The Kimball and Inmon data warehouse methodologies
   • Summary
9. Approaches to Data Ingestion
   • ETL versus ELT
   • Reverse ETL
   • Data governance
   • Summary
10. The Modern Data Warehouse
    • The MDW Architecture
    • Pros and Cons of the MDW Architecture
    • Combining the RDW and Data Lake
    • Stepping Stones to the MDW
    • Case Study: Wilson & Gunkerk’s Strategic Shift to an MDW
    • Summary
11. Data Fabric
    • The Data Fabric Architecture
    • Why Transition from an MDW to a Data Fabric Architecture?
    • Potential Drawbacks
    • Summary
12. Data Lakehouse
    • Delta lake features
    • Performance improvements
    • The data lakehouse architecture
    • What if you skip the relational data warehouse?
    • Relational serving layer
    • Summary
13. Data mesh foundation
    • A decentralized data architecture
    • Data mesh hype
    • Dehghani’s four principles of a data mesh
    • The “pure” data mesh
    • Data domains
    • Data mesh logical architecture
    • Example domains
    • Summary
14. Should you adopt data mesh? Myths, concerns, and the future
    • Myths
    • Concerns
    • Organizational assessment: Should you adopt a data mesh?
    • Recommendations for implementing a successful data mesh
    • The future of data mesh
    • Conclusion: Zooming out: understanding data architectures and their application
15. People and process
    • Team organization: Roles and responsibilities
    • Why projects fail: Pitfalls and prevention
    • Why projects succeed
    • Conclusion
16. Technologies
    • Choosing a platform
    • Cloud service models
    • Software frameworks
    • Conclusion