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

Why create a data strategy?

There are many reasons why an organization needs a data strategy. The most obvious one is to have a plan on how to become data-driven in order to generate revenue and achieve cost savings. Enterprises have learned from the pitfalls of not managing data as an asset and data product. They have lost customer opportunities and revenue from business decisions made with untrustworthy data or experienced high costs when developing customer goods. In Chapter 2, Show Me the Business Value, we discussed examples such as costs associated with regulatory fines due to lack of Data Privacy enforcement or lost revenue from bad metadata and Data Quality. These factors lead us to the need for a well-thought-out and structured data strategy that manages data effectively and is aligned with the enterprise’s business goals.

Not having a data strategy document can lead to the following negative outcomes:

  • An immature level of organizational readiness
  • Incorrect...