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 Fabric with DataOps

To support one of the key principles of a Data Fabric architecture, data are assets that evolve into Data Products, Data Fabric needs to be married with DataOps principles that focus on quality control and efficiencies in the development and delivery of data. DataOps applies principles in how data and data pipelines are developed, such as with embedded automation and orchestration. It expects the testing of data, assumptions, and integrations at a grand scale. It establishes quality controls from initial data transport from the source to the destination(s), and then after that, executes ongoing data monitoring. Data is fluid; the business evolves and requirements change, which requires proactive monitoring and analysis to ensure the successful delivery of data to consumers.

The DataOps phases are iterative and are not always executed in sequence. Sometimes, a phase may be executed multiple times. For example, during testing, you might find data validation...