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

What this book covers

Chapter 1, Introducing Data Fabric, presents an introduction to the definition of Data Fabric architecture. It offers a position on what Data Fabric is and what it isn’t. Key characteristics and architectural principles are explained. Essential concepts and terminology are defined. The business value statement of Data Fabric architecture is discussed and the core building blocks that make up its design are established.

Chapter 2, Show Me the Business Value, is a chapter focused on providing a business point of view on the benefits of Data Fabric architecture. It establishes the business value the architecture offers by explaining how the building blocks that make up a Data Fabric design address pain points faced by enterprises today. Data Fabric architecture takes a strategic and multi-faceted approach to achieve data monetization. Real-life examples have been positioned on the impact of not having the right level of focus provided by each of Data Fabric’s building blocks. Finally, a perspective is offered on how Data Fabric architecture can be leveraged by large, medium-sized, and small organizations.

Chapter 3, Choosing between Data Fabric and Data Mesh, provides an overview of the key principles of Data Mesh architecture. Both Data Fabric and Data Mesh are discussed, including where they share similar objectives and where they take different but complementary approaches. Both architectures represent sophisticated designs focused on data trust and enable the high-scale sharing of quality data. This chapter closes with a view on how Data Fabric and Data Mesh can be used together to achieve rapid data access, high-quality data, and automated Data Governance.

Chapter 4, Introducing DataOps, introduces the DataOps framework. It discusses the business value it provides and describes the 18 driving principles that make up DataOps. The role of data observability and its relationship to the Data Quality and Data Governance pillar is explained. This chapter concludes by explaining how to apply DataOps as an operational model for Data Fabric architecture.

Chapter 5, Building a Data Strategy, kicks off the creation and implementation of a data strategy document. It describes a data strategy document as a visionary statement and a plan for profitable revenue and cost savings. You will familiarize yourself with the different sections that should be defined in a data strategy document, and have a reference of three data maturity frameworks to use as input in a data strategy. The chapter ends with tips on how Data Fabric architecture can be positioned as part of a data strategy document.

Chapter 6, Designing a Data Fabric Architecture, sets the foundation for the design of a Data Fabric architecture. It introduces key architecture concepts and architecture principles that compose the logical data architecture of a Data Fabric. The three architecture layers, Data Governance, Data Integration, and Self-Service, in a Data Fabric architecture are introduced. The objectives of each layer are highlighted, with a discussion on the necessary capabilities represented as components.

Chapter 7, Designing Data Governance, dives into the design of the Data Governance layer of a Data Fabric architecture. Key architecture patterns, such as metadata-driven and event-driven architectures, are discussed. The architecture components, such as active metadata, metadata knowledge graphs, and life cycle governance, are explained. The chapter ends with an explanation of how the Data Governance layer executes and governs data at each phase in its life cycle.

Chapter 8, Designing Data Integration and Self-Service, drills into the design of the two remaining architecture layers in a Data Fabric, Data Integration and Self-Service. The Data Integration layer is reviewed, which focuses on the development of data with a DataOps lens. The Self-Service layer is also discussed, including how it aims to democratize data. An understanding is provided of how both architecture layers work with each other, and how they rely on the Data Governance layer. At the end of the chapter, a Data Fabric reference architecture is presented.

Chapter 9, Realizing a Data Fabric Technical Architecture, positions a technical Data Fabric architecture as modular and composable, consisting of several tools and technologies. The required capabilities and the kinds of tools to implement each of the three layers in a Data Fabric architecture are discussed. Two use cases are reviewed – distributed data management via Data Mesh and regulatory compliance – as examples of how to apply a Data Fabric architecture. The chapter ends by presenting a Data Fabric with Data Mesh technical reference architecture.

Chapter 10, Industry Best Practices, presents 16 best practices in data management. Best practices are grouped into four categories: Data Strategy, Data Architecture, Data Integration and Self-Service, and Data Governance. Each best practice is described and has a why should you care statement.