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

Building a Data Strategy

A data strategy articulates an organization’s plan for reaching high data maturity. Data maturity is the measurement of an organization’s expertise in applying data capabilities to achieve business value. It first requires establishing a data maturity baseline and identifying a target data maturity level. Progress needs to be measured frequently with course correction as needed. Continuous and iterative progress are the objectives of a data strategy. Achieving success with a data strategy requires a focus on data management, Data Governance, Data Privacy, Data Protection, Data Security, and other critical aspects for an organization to succeed in its digital transformation journey. There needs to be careful thought on how to execute and implement a data strategy.

In this chapter, we’ll guide you in creating a data strategy that is applicable across industries. It offers tips to create, execute, and implement a data strategy. It also...