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

Creating a data strategy

A data strategy document’s focus is driven by an organization’s business goals, current as-is data maturity level, aspired data maturity, and associated capabilities. As you have seen in this book, the common theme is always Data Governance to achieve data integrity, which is a required topic in a data strategy document. The other is overall data management. Data management can be broken up into different compartments such as Data Integration, data operations, data architecture, and so on. Regardless of which focus areas are added, what’s critical is to ensure it always ties back to the business. It needs to address how to achieve the desired level of data maturity and how the associated capabilities will accelerate an enterprise’s business objectives and vision. One common pitfall that I have seen over and over when working with brilliant technical practitioners is the inability to tie technology back to business value. It’...