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 data strategy captures an organization’s starting point, journey, goals, solutions, and target destination in order to become a data-driven enterprise. Organizations first need to recognize and understand how and why to manage data as an asset and a Data Product.

In this chapter, we discussed the value of a data strategy document enabling organizations to have a vision and plan to achieve profitable revenue and cost savings by leveraging data. We provided guidance on creating a data strategy and emphasized that it must have a business point of view across people, processes, and technology. We highlighted three popular data maturity frameworks (DMBOK, DCAM, and Gartner’s) and recommended completing a data maturity assessment as input for a data strategy. We also covered the implementation of a data strategy document and offered an example that aligned DAMA’s data maturity areas with a Data Fabric. Data management, Data Governance, Data Privacy, and...