Deciphering Data Architectures
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
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