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


Data constitutes facts, statistics, and information based on real-world entities and events. The word fabric represents a body of material with texture and structure, such as silk cloth. These two keywords, Data Fabric, create a representation of disparate data that has been connected by a data architecture driven by governance, active metadata, automated data integration, and self-service. In today’s big data era, there are many complexities faced by enterprises looking to become data driven. Many of these issues, such as data silos, agility, lack of collaboration between business and IT, high maintenance costs, data breach, and data integrity, revolve around the large volume and velocity of proliferated data. Data Fabric is a mature, composable data architecture that faces these complexities head-on to enable the management of data at a high scale with established business value.

I wrote this book to introduce a slightly different perspective on the definition of Data Fabric architecture. The view I offer is flexible and use case agnostic and supports diverse data management styles, operational models, and technologies. I describe Data Fabric architecture as taking a people, process, and technology approach that can be applied in a complementary manner with other trending data management frameworks, such as Data Mesh and DataOps. The main theme of this book is to provide a guide to the design of Data Fabric architecture, explain the foundational role of Data Governance, and provide an understanding of how Data Fabric architecture achieves automated Data Integration and Self-Service. The technique I use is by describing “a day in the life of data” as it steps through the phases of its life cycle: create, ingest, integrate, consume, archive, and destroy. I talk about how each layer in Data Fabric architecture executes in a high-performing and thorough manner to address today’s big data complexities. I provide a set of guidelines, architecture principles, best practices, and key concepts to enable the design and implementation of a successful Data Fabric architecture.

The perspective I offer is based on decades of experience in the areas of Enterprise Architecture, Data Architecture, Data Governance, and Product Management. I remember when I started my career in Data Governance, I faced many challenges convincing others of the business value that successful data management with Data Governance achieves. I saw what many others failed to see at that time, and that was when I knew data was my passion! Since then, I’ve broadened and increased my knowledge and experience. I have learned from brilliant thought leaders at IBM and a diverse set of clients. All these experiences have shaped the frame of reference in this book.

As technologists, we are very passionate about our points of view, ideas, and perspectives. This is my point of view on what a Data Fabric architecture design represents, which aims to achieve significant business value while addressing the complexities enterprises face today.


The views expressed in the book belong to the author and do not necessarily represent the opinions or views of their employer, IBM.