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 Integration and Self-Service best practices

Data Integration and Self-Service focus on the development and delivery of data throughout its life cycle, handling inbound and outbound data processing and facilitating data access.

The following is a list of best practices in data processing and Self-Service data access.

Best practice 10

Apply DataOps principles to the development and delivery of data.

DataOps is a best practice framework that accelerates the development of data and quality across its entire life cycle with high efficiency and quality. This is especially important when integrating data across distributed complex systems and environments. Concepts such as quality control, version control, data orchestration, continuous integration/continuous deployment (CI/CD), automated testing, automated deployments, and data monitoring are applied to data and metadata. Feedback loops establish an open communication channel for customers to drive ongoing quality improvements...