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

Applying a Data Fabric architecture with a DataOps framework dramatically raises the bar to deliver data with high agility, quality, and governance at a lower cost. In this chapter, we provided an introduction to DataOps, its value, and the 18 driving principles. We briefly introduced Agile, SPC, and DevOps from which DataOps borrows many of its principles. We reviewed the distinction between traditional Data Quality and modern Data Quality, and how modern Data Quality can leverage a foundational Data Quality framework. We also defined data observability and its relationship to Data Quality. We concluded by conceptualizing the use of a Data Fabric architecture together with a DataOps framework. Both offer tremendous value and should be applied in the life cycle management of data.

At this point in the book, we have introduced Data Fabric, Data Mesh, DataOps, and foundational Data Governance concepts. In the next chapter, we will focus on a key business artifact, a data strategy...