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

Summary

A technical Data Fabric architecture is modular and composable of several tools and technologies. There are three logical layers in a Data Fabric architecture consisting of Data Governance, Data Integration, and Self-Service.

In this chapter, we reviewed capabilities and the kinds of tools that could be used to implement each layer in a Data Fabric architecture. We have also talked about the requirements and assumptions in two use cases: distributed data management via Data Mesh, and regulatory compliance. A reference architecture was presented that offers a point of view on how to apply both Data Fabric with Data Mesh architectures with a federated operational model. These architectures have similar objectives and call for similar capabilities where Data Fabric is active metadata-driven and takes a more technical approach when compared to Data Mesh, which is more organizationally driven.

In the next and last chapter, we will review industry best practices and recap...