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

Data Fabric with Data Mesh reference architecture

Data Mesh and Data Fabric both aim to achieve similar objectives; their approach is different but complementary. A Data Mesh architecture can build on a Data Fabric’s technical approach. There is a misconception in the market today that only one architecture should be selected to execute data management. The reality is that there should be different styles of architecture incorporated into data management to achieve success. As architects, data practitioners, and technologists, we will have different perspectives and points of view that may spark debates. That’s part of us learning, sharing, and collaborating with one another and helps us grow as professionals. I offer a point of view of how a Data Fabric architecture can be used together with a Data Mesh architecture.

A Data Fabric’s active metadata-driven architecture is a differentiator that complements and accelerates the Data Mesh’s principles. Figure...