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 Governance architecture

The Data Governance layer establishes the foundational data architecture for a Data Fabric, which both the Data Integration and Self-Service layers rely on. The following are two key architecture patterns in the Data Governance layer:

  • Metadata-driven
  • Event-driven

Let’s discuss each of them in detail.

Metadata-driven architecture

Metadata in data management is defined classically as data about data. It provides context and information relevant to data so that it can be understood and used effectively. Metadata collection has a long history, dating back to the late 1900s (https://www.dataversity.net/a-brief-history-of-metadata/). The reliance on and use of metadata since then have increased drastically throughout the years with digital data management. Companies such as Google and Adobe were pioneers in tapping into the value of leveraging metadata to create a world of discoverable and usable data at a grand scale to realize...