Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently

✍ Scribed by Sonia Mezzetta

• Publisher: Packt Publishing
• Tongue: English
• Leaves: 188
• Category: Library

✦ Synopsis

Apply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles.

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Learn to design Data Fabric architecture effectively with your choice of tool
• Build and use a Data Fabric solution using DataOps and Data Mesh frameworks
• Find out how to build Data Integration, Data Governance, and Self-Service analytics architecture

Book Description

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.

What you will learn

• Understand the core components of Data Fabric solutions
• Combine Data Fabric with Data Mesh and DataOps frameworks
• Implement distributed data management and regulatory compliance using Data Fabric
• Manage and enforce Data Governance with active metadata using Data Fabric
• Explore industry best practices for effectively implementing a Data Fabric solution

Who this book is for

If you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem.

Table of Contents

1. Introducing Data Fabric
2. Show Me the Business Value
3. Choosing between Data Fabric and Data Mesh
4. Introducing DataOps
5. Building a Data Strategy
6. Designing a Data Fabric Architecture
7. Designing Data Governance
8. Designing Data Integration and Self-Service
9. Realizing a Data Fabric Technical Architecture
10. Industry Best Practices

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Dedication
Contributors
Table of Contents
Preface
Part 1: The Building Blocks
Chapter 1: Introducing Data Fabric
What is Data Fabric?
What Data Fabric is
What Data Fabric is not
Why is Data Fabric important?
Drawbacks of centralized data management
Decentralized data management
Building Data Fabric architecture
Data Fabric building blocks
Data Fabric principles
The four Vs
Data Governance
Knowledge layer
Data Integration
Self-Service
Operational Data Governance models
Summary
Chapter 2: Show Me the Business Value
Digital transformation
Data monetization
Revenue
Cost savings
Data Fabric’s value proposition
Trusting your decisions with governed data
Creating a unified view of your data with intelligent Data Integration
Gaining a competitive advantage with Self-Service
Data Fabric for large, medium, and small enterprises
Large enterprise organizations
Small and medium-sized businesses
Summary
Part 2: Complementary Data Management Approaches and Strategies
Chapter 3: Choosing between Data Fabric and Data Mesh
Introducing Data Mesh
Domain ownership
Data as a product
Self-Serve data platform
Federated computational governance
Comparing Data Fabric and Data Mesh
Objectives
Data Fabric and Data Mesh’s friendship
How Data Fabric supports a federated-based organization
How Data Fabric manages data as a product
Self-Service data platform via a Data Fabric and Data Mesh architecture
Federated computational governance with Data Fabric
Summary
Chapter 4: Introducing DataOps
What is DataOps?
DataOps’ principles
The evolution of DataOps
DataOps’ dimensions
MLOps and AIOps depend on DataOps
DataOps’ value
From traditional Data Quality to data observability
Data Fabric with DataOps
Develop
Orchestrate
Test
Deploy
Monitor
Summary
Chapter 5: Building a Data Strategy
Why create a data strategy?
A data maturity framework
A data maturity assessment
Creating a data strategy
Topics in a data strategy document
Creating a data strategy document
Data strategy implementation
Summary
Part 3: Designing and Realizing Data Fabric Architecture
Chapter 6: Designing a Data Fabric Architecture
Introduction to enterprise architecture
Types of enterprise architecture
Data Fabric principles
Data Fabric architecture principles
Data Fabric architecture layers
Data Governance
Data Integration
Self-Service
Summary
Chapter 7: Designing Data Governance
Data Governance architecture
Metadata-driven architecture
EDA
Metadata as a service
Metadata collection
Metadata integration
Metadata-based events
The Data Governance layer
Active metadata
Life cycle governance
Operational models
The Data Fabric’s governance applied
The Create phase
The Ingest phase
The Integrate phase
The Consume phase
The Archive and Destroy phase
Summary
Chapter 8: Designing Data Integration and Self-Service
DataOps-based architecture
Data Integration layer
Data management
Development workflow
Self-Service layer
Data democratization
Data consumption
Data journey in a Data Fabric architecture
Phase 1 – Create phase in the Data Integration layer
Phases 2 and 3 – Ingest and Integrate phases in the Data Integration layer
Phase 4 – Consume phase in the Self-Service layer
Phase 5 – Archive and Destroy phase
Data Fabric reference architecture
Data Fabric architecture highlights
Summary
Chapter 9: Realizing a Data Fabric Technical Architecture
Technical Data Fabric architecture
Data Fabric tools
Vendor and open source tools
Use cases
Distributed data management and sharing via Data Mesh
Regulatory compliance
Data Mesh multi-plane requirements
Multi-plane architecture
Data Mesh assumptions
Data Fabric with Data Mesh reference architecture
Reference architecture explained
Federated operational model
Summary
Chapter 10: Industry Best Practices
Top 16 best practices
Data strategy best practices
Best practice 1
Best practice 2
Best practice 3
Best practice 4
Data architecture best practices
Best practice 5
Best practice 6
Best practice 7
Best practice 8
Best practice 9
Data Integration and Self-Service best practices
Best practice 10
Best practice 11
Best practice 12
Data Governance best practices
Best practice 13
Best practice 14
Best practice 15
Summary
About Packt
Other Books You May Enjoy
Index