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

Data Governance must be designed end to end across all the phases (Create, Ingest, Integrate, Consume, Archive, and Destroy) in a data life cycle. In this chapter, we defined a Data Governance architecture as being metadata-driven and event-driven We also defined what it means to manage metadata as a service, which focuses on the collection, integration, and storage of distributed metadata to derive insights and take action. We learned that a Data Governance layer consists of two components: active metadata and life cycle governance. We also understand that the brain of the Data Fabric architecture is active metadata, which is represented by a Metadata Knowledge Graph. The life cycle governance component consists of capabilities that are centered around Data Governance pillars such as Data Privacy, Protection, and Security, Data Quality, Data Lineage, MDM, and Metadata Management.

In the next chapter, we will focus on the two other layers in a Data Fabric architecture:...