Book Image

Principles of Data Fabric

By : Sonia Mezzetta
Book Image

Principles of Data Fabric

By: Sonia Mezzetta

Overview of this book

Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered. The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You’ll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you’ll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles. By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution.
Table of Contents (16 chapters)
Part 1: The Building Blocks
Part 2: Complementary Data Management Approaches and Strategies
Part 3: Designing and Realizing Data Fabric Architecture

Choosing between Data Fabric and Data Mesh

Just like Data Fabric, Data Mesh has become a buzzword in data management. Data Mesh focuses on principles such as managing data as a product, Self-Service, and providing a federated model at the organization and Data Governance level. The premise is to dismantle the concept of relying on central teams for data operations and move away from centralized data management to a decentralized approach. The Data Fabric and Data Mesh architecture approaches are often confused or referred to interchangeably; however, they are two separate design concepts. The question often asked is, do we need to choose one over the other? The answer is that they are complementary, and Data Fabric can be used together with Data Mesh.

In this chapter, we’ll focus on discussing how Data Fabric and Data Mesh share similar best practices and principles, and we’ll look at where they differ. I’ll provide a perspective of Data Mesh based on Zhamak...