For those looking to be first in line when my book is available for a printed copy, I’m very excited to let you know that it is now available for pre-order on Amazon at Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh: 9781098150761: Serra, James. Note that I’m expecting the book to be available a couple months before the listed April 2nd date.

And for those at the PASS Summit this week, I’ll be doing two sessions:

Enhancing your Career: Building your Personal Brand
Thursday, Nov 16, 3:15 PM – 4:30 PM PST, room 609

In three years I went from a complete unknown to a popular blogger, speaker at PASS Summit, and a SQL Server MVP. Along the way I saw my yearly income triple. Is it because I know some secret? Is it because I am a genius? No! It is just about laying out your career path, setting goals, and doing the work. It’s about building your personal brand and stepping out of your comfort zone. It’s about overcoming your fear of taking risks. If you can do those things, you will be rewarded. I will discuss how you too can go from unknown to well-known. I will talk about building your community presence by blogging, presenting, writing articles and books, twitter, LinkedIn, certifications, interviewing, networking, and consulting and contracting. Your first step to enhancing your career will be to attend this session!

Data Warehousing Trends, Best Practices, and Future Outlook
Friday, Nov 17, 10:15 AM – 11:30 AM PST, room 606

Over the last decade, the 3Vs of data – Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. Learn: Choosing the best solution – Data Lake vs. Data Warehouse vs. Data Mart Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon Step by step approach to building an effective data warehouse architecture

I hope to see you at PASS! Stop me in the hallways if you see me and let’s chat!

The book cover and TOC:

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