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

Data Fabric principles

Let’s start with the objectives of data architecture. It pivots on how IT is used to manage the life cycle of data. It addresses data from the point it is created, stored, transformed, cleansed, and consumed to its end of life. Data architecture is the foundation and strategy that provide the backing to achieve data-driven initiatives. So, what are the best practices and principles that make a good working data architecture? It will need to exhibit more positive trade-offs when it comes to architecture decisions than negatives, as called out by the authors of Software Architecture: The Hard Parts:

…least worst collection of trade-offs – no single architecture characteristics excels as it would alone, but the balance of all the competing architecture characteristics promote project success.

A best practice is to document architecture decisions both business and technical in the form of Architecture Decision Records (ADRs) (Source: Software...