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 strategy implementation

At this stage, a data strategy document has been created. The stakeholders are all on board and the sponsor(s) is rallying the implementation of the data strategy. The next step is agreeing on how to implement a data strategy. You can leverage the data maturity assessment results and target data maturity levels as a guide. These two parameters can be used to define new improvement initiatives and implementation approaches.

As an example, if the target state is to achieve a high level of data maturity in Data Quality and Data Integration to address company-wide data breaches, then these focus areas need to be aligned with potential solutions from people, process, and technical standpoints. Some of the questions to consider when selecting a solution are as follows:

  • Can data practitioners collaborate better? Are the interactions needed between technical roles and the wider business addressed?
  • What are the necessary process changes to drive faster...