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 journey in a Data Fabric architecture

The best way to understand of how a Data Fabric architecture handles data is to look at it from a journey standpoint. We will follow the same approach as we did in explaining the Data Governance layer in Chapter 7. Technical capabilities within a Data Fabric architecture enable the development of data and its delivery. DataOps principles act as a medium in the deployment of data within the Data Integration and Self-Service layers of a Data Fabric architecture. DataOps focuses on efficiencies and quality control in the development, deployment, and delivery of data.

Data travels through five logical phases in a data life cycle across the Data Fabric architecture:

  1. Create
  2. Ingest
  3. Integrate
  4. Consume
  5. Archive and destroy

DataOps has its own cycle with specific activities that guide the development of data from the create to the consume phase. The stages in DataOps consist of development, orchestration, testing, deployment...