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

Operational Data Governance models

Data Fabric architecture supports diverse Data Governance models. It has the necessary architecture robustness, components, and capabilities to support three types of models:

  • Centralized Data Governance: A central Data Governance organization that is accountable for decision-making, data policy management, and enforcement across all business units within an organization. This represents a traditional model.
  • Decentralized Data Governance: An organization where a business domain is accountable for and manages the Data Governance program. The business domain is accountable only for their domain’s decision-making and data policy management and enforcement. They operate independently in terms of data management and Data Governance from the rest of the organization.
  • Federated Data Governance: In a federated Data Governance model, a federated group of business domain representatives across an organization and governance roles is accountable for global-level concerns and decision-making on Data Governance matters. The federated governance team is not responsible for enforcement or local policy management. Business domains manage their data, local data policies, and enforcement.

Let’s summarize what we have covered in this chapter in the next section.