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

Data architecture best practices

What are the best practices in designing a quality data architecture? It’s a careful balance between positive trade-offs versus negative trade-offs.

The following is a list of best practices in data architecture.

Best practice 5

Assess and document architecture decisions.

A trade-off analysis should be completed when designing a data architecture that carefully evaluates each option, weighing the pros and cons. Documented architecture decisions that capture the options considered and the rationale of going one way or another should be captured as Architecture Decision Records (ADRs). This offers tribal knowledge in the decision-making process, which needs to cover both business and technical reasoning behind the decisions made. ADRs create better collaboration and understanding for future changes required in a data architecture especially as data roles change within teams.

Why should you care?

Trade-off analysis and ADRs avoid...