Data Mesh: Delivering Data-Driven Value at Scale

✍ Scribed by Zhamak Dehghani

• Publisher: O'Reilly Media, Inc.
• Year: 2021
• Tongue: English
• Leaves: 90
• Edition: 3
• Category: Library

✦ Synopsis

Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice.

Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how.

Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures
Analyze the landscape's underlying characteristics and failure modes
Get a complete introduction to data mesh principles and its constituents
Learn how to design a data mesh architecture
Move beyond a monolithic data lake to a distributed data mesh

✦ Table of Contents

Cover
Starburst Data
Copyright
Table of Contents
Part I. Why Data Mesh?
Chapter 1. The Inflection Point
Great Expectations of Data
The Great Divide of Data
Operational Data
Analytical Data
Analytical and Operational Data Misintegration
Scale, Encounter of a New Kind
Beyond Order
Approaching the Plateau of Return
Recap
Chapter 2. After The Inflection Point
Embrace Change in a Complex, Volatile and Uncertain Business Environment
Align Business, Tech and Now Analytical Data
Close The Gap Between Analytical and Operational Data
Localize Data Change to Business Domains
Reduce Accidental Complexity of Pipelines and Copying Data
Sustain Agility in the Face of Growth
Remove Centralized and Monolithic Bottlenecks of the Lake or the Warehouse
Reduce Coordination of Data Pipelines
Reduce Coordination of Data Governance
Enable Autonomy
Increase the Ratio of Value from Data to Investment
Abstract Technical Complexity with a Data Platform
Embed Product Thinking Everywhere
Go Beyond The Boundaries
Recap
Chapter 3. Before The Inflection Point
Evolution of Analytical Data Architectures
First Generation: Data Warehouse Architecture
Second Generation: Data Lake Architecture
Third Generation: Multimodal Cloud Architecture
Characteristics of Analytical Data Architecture
Monolithic
Monolithic Architecture
Monolithic Technology
Monolithic Organization
The complicated monolith
Technically-Partitioned Architecture
Activity-oriented Team Decomposition
Recap
Part II. What is Data Mesh
Chapter 4. Principle of Domain ownership
Apply DDD’s Strategic Design to Data
Domain Data Archetypes
Source-aligned Domain Data
Aggregate Domain Data
Consumer-aligned Domain Data
Transition to Domain Ownership
Push Data Ownership Upstream
Define Multiple Connected Models
Embrace the Most Relevant Domain, and Don’t Expect the Single Source of Truth
Hide the Data Pipelines as Domains’ Internal Implementation
Recap
Chapter 5. Principle of Data as a Product
Apply Product Thinking to Data
Baseline usability characteristics of a data product
Transition to Data as a Product
Include Data Product Ownership in Domains
Recap
Prospective Table of Contents (Subject to Change)
Part I : Why Data Mesh?
Chapter 1: The Inflection Point
Chapter 2: After the Inflection Point
Chapter 3: Before The Inflection Point
Part II: What Is Data Mesh?
Chapter 4: Principle of Domain Ownership
Chapter 5: Principle of Data as a Product
Chapter 6: Principle of Self-Serve Data Platform
Chapter 7: Principle of Federated Computational Governance
Part III: How to Design Data Mesh Architecture?
Chapter 8: The Logical Architecture
Chapter 9: Data Product Quantum Blueprint
Chapter 10: The Multi-Plane Data Platform
Part IV: How to Get Started With Data Mesh
Chapter 11: Execution Model
Chapter 12: Organization Design
Chapter 13: What Comes Next