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

DataOps-based architecture

DataOps is a mature data management framework with best practices and principles adopted from software engineering and manufacturing frameworks such as Agile, DevOps, and statistical process control. In DataOps, these successful best practices are applied in the context of data management to achieve fast, high-quality, and cost-efficient data delivery. DataOps continuously monitors data post-deployment to keep an eye on its pulse to check its health. In my view, a DataOps discipline is a necessary framework that drastically accelerates a Data Fabric architecture. DataOps was introduced in Chapter 4, Introducing DataOps.

What’s the difference between DataOps and data engineering?

These terms are often used interchangeably; however, they are not the same thing. The assumption made when referring to DataOps is that it refers to daily data operations within a business. DataOps is a data management discipline with established principles that achieve...