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 Integration layer

The Data Integration layer of a Data Fabric architecture represents a set of capabilities in the form of services, tools, and data environments (development, test, and production) that support the development cycle of data to achieve downstream consumption. The Data Integration layer is composed of two pillars: data management and development workflow. Each pillar represents a group of capabilities and technologies that focus on realizing a phase in the life cycle of data. The end goal of the Data Integration layer is to technically support the ingestion, integration, and delivery of data. It needs to support the necessary provisioning and configuration to enable data to be consumed.

How does a Data Fabric support data assets, data-related assets, and Data Products?

Data can be a data asset or a data-related asset that can be grouped to represent one of the several assets in a Data Product. What differs between a data asset, a data-related asset, and a...