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

Self-Service layer

The Self-Service layer’s main objective is to democratize data. It enables the exploration and use of high-value data. The Self-Service layer continues from where the Data Integration layer left off. A Self-Service architecture has a focused role in enabling the sharing and consumption of data via Self-Service processes. It works with the Data Governance layer to govern and protect data while unlocking its use across diverse systems. Metadata- and event-driven architectures introduced in the Data Governance layer are relied upon. The Self-Service layer also relies on the Data Integration layer to facilitate, via automated means, the delivery of data to data consumers. Data consumption is typically read-only, however, exceptions are possible and need to be designed into the architecture. An example is the need for special privileges for superusers.

There is also the notion of enabling data consumers via Self-Service data composability capabilities to customize...