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)
1
Part 1: The Building Blocks
4
Part 2: Complementary Data Management Approaches and Strategies
8
Part 3: Designing and Realizing Data Fabric Architecture

Data Fabric architecture layers

Data Fabric architecture follows the nine principles discussed in the previous section. These principles establish the bedrock for a Data Fabric architecture that addresses data silos, enables data democratization, and creates a connected and intelligent data ecosystem of trusted, secure, and reliable data that supports data producers and data consumers. In Chapter 1, Introducing Data Fabric, we discussed Data Fabric design as having three building blocks:

  • Data Governance
  • Data Integration
  • Self-Service

Let’s represent these building blocks as layers in a Data Fabric architecture with specific responsibilities and supporting components:

  • Data Governance enables active Metadata Management and automated life cycle governance. Its knowledge layer is represented by a Metadata Knowledge Graph.
  • Data Integration handles inbound and outbound data management, data processing, and data engineering. This layer relies on active...