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


A Data Fabric architecture manages, governs, and supports the life cycle of data. It embraces a shift-left approach by infusing metadata and event-driven architectures with foundational Data Governance and data sharing capabilities. In this chapter, we reviewed the Data Integration layer focused on the development of data with a DataOps lens across the stages (develop, orchestrate, test, deploy, and monitor). We also reviewed the Self-Service layer focused on the democratization of data to achieve data sharing. Both the Data Integration and the Self-Service layers are of extreme importance; however, they heavily rely on embedded and automated governance in the Data Governance layer. All three layers seamlessly work together to manage, govern, and share data with maximum business value. They represent the constructs that compose a Data Fabric architecture.

In the next chapter, we will review how to design a technical architecture for a Data Fabric.