Deciphering Data Architectures

by James Serra

Data Science

Book Details

Book Title

Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh Edition: 1

Author

James Serra

Publisher

O'Reilly Media

Publication Date

2024

ISBN

9781098150761

Number of Pages

278

Language

English

Format

PDF

File Size

6.72MB

Subject

Computers > Databases

Table of Contents

  • Copyright
  • Table of Contents
  • Foreword
  • Preface
  • Part I. Foundation
  • Chapter 1. Big Data
  • What Is Big Data, and How Can It Help You?
  • Data Maturity
  • Self-Service Business Intelligence
  • Summary
  • Chapter 2. Types of Data Architectures
  • Evolution of Data Architectures
  • Relational Data Warehouse
  • Data Lake
  • Modern Data Warehouse
  • Data Fabric
  • Data Lakehouse
  • Data Mesh
  • Summary
  • Chapter 3. The Architecture Design Session
  • What Is an ADS?
  • Why Hold an ADS?
  • Before the ADS
  • Conducting the ADS
  • After the ADS
  • Tips for Conducting an ADS
  • Summary
  • Part II. Common Data Architecture Concepts
  • Chapter 4. The Relational Data Warehouse
  • What Is a Relational Data Warehouse?
  • What a Data Warehouse Is Not
  • 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
  • Chapter 5. Data Lake
  • What Is a Data Lake?
  • Why Use a Data Lake?
  • Bottom-Up Approach
  • Best Practices for Data Lake Design
  • Multiple Data Lakes
  • Summary
  • Chapter 6. Data Storage Solutions and Processes
  • Data Storage Solutions
  • Data Processes
  • Summary
  • Chapter 7. Approaches to Design
  • Online Transaction Processing Versus Online Analytical Processing
  • Operational and Analytical Data
  • Symmetric Multiprocessing and Massively Parallel Processing
  • Lambda Architecture
  • Kappa Architecture
  • Polyglot Persistence and Polyglot Data Stores
  • Summary
  • Chapter 8. Approaches to Data Modeling
  • Relational Modeling
  • Dimensional Modeling
  • Common Data Model
  • Data Vault
  • The Kimball and Inmon Data Warehousing Methodologies
  • Methodology Myths
  • Summary
  • Chapter 9. Approaches to Data Ingestion
  • ETL Versus ELT
  • Reverse ETL
  • Batch Processing Versus Real-Time Processing
  • Data Governance
  • Summary
  • Part III. Data Architectures
  • Chapter 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
  • Chapter 11. Data Fabric
  • The Data Fabric Architecture
  • Why Transition from an MDW to a Data Fabric Architecture?
  • Potential Drawbacks
  • Summary
  • Chapter 12. Data Lakehouse
  • Delta Lake Features
  • Performance Improvements
  • The Data Lakehouse Architecture
  • What If You Skip the Relational Data Warehouse?
  • Relational Serving Layer
  • Summary
  • Chapter 13. Data Mesh Foundation
  • A Decentralized Data Architecture
  • Data Mesh Hype
  • Dehghani’s Four Principles of Data Mesh
  • The “Pure” Data Mesh
  • Data Domains
  • Data Mesh Logical Architecture
  • Different Topologies
  • Data Mesh Versus Data Fabric
  • Use Cases
  • Summary
  • Chapter 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
  • Zooming Out: Understanding Data Architectures and Their Applications
  • Summary
  • Part IV. People, Processes, and Technology
  • Chapter 15. People and Processes
  • Team Organization: Roles and Responsibilities
  • Why Projects Fail: Pitfalls and Prevention
  • Tips for Success
  • Summary
  • Chapter 16. Technologies
  • Choosing a Platform
  • Cloud Service Models
  • Software Frameworks
  • Summary
  • Index
  • About the Author
  • Colophon