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

What is DataOps?

DataOps is a framework that applies best practices, processes, and technologies using a collaborative approach to achieve fast, high-quality, and cost-efficient data delivery. Similar to Data Fabric, DataOps is not a tool or specific technology. Rather, it’s a set of principles focused on managing data as code, which in turn enables a deep level of automation necessary for scaling out data management. It emphasizes teamwork across a diverse set of data roles working on data analytics to eliminate misalignment between teams. Its bedrock is the ongoing quality monitoring of both data and processes to achieve customer satisfaction and efficiency. It advocates reusability, iterative short deployment cycles, and feedback loops to achieve business and customer excellence.

DataOps is applicable to raw and business-ready data. It streamlines the development, testing, deployment, and monitoring of data and its pipelines by applying proven, successful quality control...