My data architecture book now has 15 chapters available!
Only one more chapter to go! 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. The fully finished book should be available for download or for a printed copy by the end of January. The cover will feature the Discus fish (O’Reilly is known for having different animals on their cover).
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15 of 16 chapters are out. Here is the likely final TOC:
- Big Data
- What is Big Data and how can it help you?
- Data maturity
- Self-Service Business Intelligence
- Summary
- Types of Data Architectures
- Evolution of data architectures
- Relational Data Warehouse
- Data Lake
- Modern Data Warehouse
- Data Fabric
- Data Lakehouse
- Data Mesh
- Summary
- The Architecture Design Session
- What is an ADS?
- Why hold an ADS?
- Before the ADS
- Conducting the ADS
- After the ADS
- Tips
- Summary
- 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
- 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
- Data Storage Solutions and Process
- Data storage solutions
- Data processes
- Summary
- 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
- Approaches to Data Modeling
- Relational modeling
- Dimensional Modeling
- Common Data Model (CDM)
- Data Vault
- The Kimball and Inmon data warehouse methodologies
- Summary
- Approaches to Data Ingestion
- ETL versus ELT
- Reverse ETL
- Data governance
- Summary
- 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
- Data Fabric
- The Data Fabric Architecture
- Why Transition from an MDW to a Data Fabric Architecture?
- Potential Drawbacks
- Summary
- Data Lakehouse
- Delta lake features
- Performance improvements
- The data lakehouse architecture
- What if you skip the relational data warehouse?
- Relational serving layer
- Summary
- 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
- 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
- People and process
- Team organization: Roles and responsibilities
- Why projects fail: Pitfalls and prevention
- Why projects succeed
- Conclusion
- Technologies
- Choosing a platform
- Cloud service models
- Software frameworks
- Conclusion
It’s 236 printed pages so far. Check it out here. Soon the book will go into “production” and be updated by a grammar editor along with the figures being rewritten, TOC and Index created, covers drawn, and then it’s off to the presses!
This 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. Please send me any feedback on the book to jamesserra3@gmail.com. Would love to hear what you think!
I am much interested in this book