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

Introducing DataOps

DataOps is a collaborative data management framework that can be applied to a Data Fabric architecture by managing the stages from the creation to the deployment of data. It offers an operational model for data to deliver quality analytics. DataOps and Data Fabric can be executed independently of one another, but when used together, they create a high-quality, automated, cost-effective, and well-governed data environment on steroids. This chapter will introduce the DataOps framework and its principles. It will also look at its evolution from other popular frameworks such as DevOps, Statistical Process Control (SPC), and Agile. We will discuss the applicability of Data Quality to data observability, a subcomponent of DataOps, and the alignment of DataOps and Data Fabric.

By the end of this chapter, you will understand the basics of DataOps and how it aligns with Data Fabric.

In this chapter, we will cover the following topics:

  • What is DataOps?
  • DataOps...