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)
Part 1: The Building Blocks
Part 2: Complementary Data Management Approaches and Strategies
Part 3: Designing and Realizing Data Fabric Architecture

Metadata as a service

Metadata as a service needs to be designed into a platform to drive automation and enable data sharing, governance, and Data Integration. The collection of metadata needs to take place throughout the life cycle of data, across all data management functions such as the development and deployment of data, data ingestion, Data Integration, and data consumption until its end of life. Metadata collection at every stage of the data life cycle enables us to make intelligent decisions to increase business value. In the following section, we will dive into some important points about metadata collection.

Metadata collection

To enable data consumption and enforce the right level of Data Governance, data must be discoverable and understood. This starts by collecting metadata wrapped around data. Metadata should be leveraged to create a 360-degree view that represents everything there is to know about assets, whether data assets, data related assets, and Data Products...