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  • Book Overview & Buying Principles of Data Fabric
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Principles of Data Fabric

Principles of Data Fabric

By : Sonia Mezzetta
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Principles of Data Fabric

Principles of Data Fabric

4 (2)
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)
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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

In this chapter, we defined Data Fabric and its characteristics–highly automated, use case agnostic, Self-Service, strong Data Governance, Privacy, and Protection, supports decentralized, federated, and centralized Data Governance, event and active metadata driven, interoperable, and has a composable architecture. We discussed why this architecture is important, including its ability to effectively address data silos, data democratization, establish trusted fit for purpose data while addressing business and technical needs. We introduced the core building blocks of a Data Fabric design (Data Governance, Data Integration, and Self-Service) and its principles. We reviewed the role and value of Data Fabric’s knowledge layer, uses active metadata as the glue that intelligently connects data across an ecosystem. We also defined the various Data Governance models that Data Fabric supports.

In the next chapter, we will dive into the business value that Data Fabric architecture offers. We will discuss how the components that make up the backbone of Data Fabric derive business value that leads to profit and cost savings.

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