Deciphering Data Architectures Choosing Between a Modern Data Warehouse Data Fabric Data Lakehouse and Data Mesh 1st Edition by James Serra ISBN 1098150767 9781098150761

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ISBN 10: 1098150767 
ISBN 13: 9781098150761
Author: James Serra

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they’re also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each.

James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You’ll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you’ll be able to determine the most appropriate data architecture for your needs. With this book, you’ll:

• Gain a working understanding of several data architectures
• Learn the strengths and weaknesses of each approach
• Distinguish data architecture theory from reality
• Pick the best architecture for your use case
• Understand the differences between data warehouses and data lakes
• Learn common data architecture concepts to help you build better solutions
• Explore the historical evolution and characteristics of data architectures
• Learn essentials of running an architecture design session, team organization, and project success factors

  Free from product discussions, this book will serve as a timeless resource for years to come.

Deciphering Data Architectures Choosing Between a Modern Data Warehouse Data Fabric Data Lakehouse and Data Mesh 1st Table of contents:

• Chapter One: The Modern Data Warehouse

  • Defining the Data Warehouse Concept
  • Benefits and Challenges of Traditional Data Warehouses
  • Key Features and Capabilities of a Modern Data Warehouse
  • When to Choose a Data Warehouse

• Chapter Two: Data Fabric: The New Era of Data Integration

  • What is Data Fabric?
  • Key Components and Architecture of Data Fabric
  • The Role of AI and Automation in Data Fabric
  • Benefits and Limitations of Data Fabric
  • Use Cases and Implementation Considerations

• Chapter Three: Data Lakehouse: Bridging the Gap Between Data Lakes and Warehouses

  • Understanding the Data Lakehouse Concept
  • How Data Lakehouses Combine the Best of Data Lakes and Warehouses
  • Key Features and Technologies Behind Data Lakehouses
  • Pros and Cons of Data Lakehouses
  • Deciding if a Data Lakehouse is Right for Your Organization

• Chapter Four: Data Mesh: A Decentralized Approach to Data Architecture

  • Defining the Data Mesh Paradigm
  • Core Principles of Data Mesh Architecture
  • The Role of Domain-Oriented Data Ownership
  • Benefits and Challenges of Adopting a Data Mesh
  • When to Consider a Data Mesh for Scalable Data Solutions

• Chapter Five: Comparing Data Architectures: Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh

  • Key Differences in Design and Architecture
  • Evaluating Scalability, Flexibility, and Cost
  • Choosing the Right Architecture Based on Data Needs
  • Aligning Business Goals with Data Strategy

• Chapter Six: Data Governance and Security Across Architectures

  • Data Governance in Data Warehouses, Fabrics, Lakehouses, and Meshes
  • Privacy, Security, and Compliance Considerations
  • Best Practices for Data Security in Modern Architectures

• Chapter Seven: Real-World Case Studies and Success Stories

  • Industry Examples of Data Warehouse, Fabric, Lakehouse, and Mesh Implementations
  • Lessons Learned from Leading Organizations
  • Key Success Factors in Choosing the Right Architecture

• Chapter Eight: The Future of Data Architectures

  • Emerging Trends in Data Management
  • The Role of AI, ML, and Automation in Future Data Architectures
  • How the Evolving Data Landscape Will Shape Enterprise Data Strategies

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James Serra,Deciphering Data,Architectures,Modern Data Warehouse,Data Fabric,Lakehouse