We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.
 

Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.

• 
  •  Get a complete introduction to data mesh principles and its constituents
• •  Design a data mesh architecture
• •  Guide a data mesh strategy and execution
• •  Navigate organizational design to a decentralized data ownership model
• •  Move beyond traditional data warehouses and lakes to a distributed data mesh
  
  In the handful of years since the creation of data mesh, it has received tremendous support from lead adopter companies implementing it. It has encouraged vendors to attempt to adapt their products to suit data mesh implementations. It has created a thriving learning community to share their experiences.
   

This book creates a foundation of the objectives of data mesh, why we should bother, its first principles. We look at how to apply the first principles to create a high-level architecture and leave you with tools to execute its implementation and shift the organization and culture.
 

This book is written for people with a diverse set of roles and skill sets. Data mesh is a paradigm shift, and it needs the collective effort of many complementary roles and disciplines to make it a reality for any organization, from architects, practitioners and infrastructure engineers to product managers, data leaders, and executives.
 

Here is a quick summary of readers’ personas and what they can take away from this book:

• •  Analytical data users such as data scientists and analysts should read this book to understand what data mesh enables for them and how they become part of this movement as an active member of the mesh, in turn, providing their insights and inferences as new data products are shared on the mesh.
• 
  •  Data providers, such as application teams or data engineers, should read this book to understand how data mesh integrates the two planes of operational and analytical data and applications. They will see and how their roles move into cross-functional domain teams and what kind of architecture they will build to enable data mesh.
   
• •  Infrastructure product owners, architects, and engineers should read this book to understand the role and design of a self-serve data platform to create a well-integrated set of services that enable data sharing decentrally at scale and by cross-functional domain teams.
• 
  •  Data governance teams should read this book to understand the new structure and approach to achieving governance objectives in a way that is conducive to independent domain ownership of data, removes organizational bottlenecks, and relies heavily on automation and computation. This book introduces a new role and shape for data governance.
   
• •  Data leaders, managers, and executives should read this book to understand the paradigm shift that is coming and learn to formulate a data strategy based on data mesh, execute its transformation, and cultivate their organization along the way.

This book is written for people with backgrounds in traditional data and analytics, and people who have been more focused on software and application delivery. Data mesh closes the gap between these two groups.
 

If you come from a traditional data background, perhaps having been a data engineer or data analyst, I encourage you to suspend your past biases. Be open to new ways of solving the problem of analytical data management and processing. Embrace computation and automation as an indispensable companion of data.
 

If you come from an application development background, software architecture or application infrastructure engineering, read this book with empathy for data and analytics. See yourself as part of the solution for sharing data and getting value from data to improve your applications. Imagine a new future where data work and application development are two complementary pieces of making your solutions successful.

About the Author

Zhamak Dehghani is a director of technology at ThoughtWorks, focusing on distributed systems architecture --- big data and operational systems -- in the enterprise. She’s a member of the company’s Technology Advisory Board and contributes to the creation of ThoughtWorks’s Technology Radar. She is an advocate for decentralization of all things - architecture, data and ultimately power. She is the founder of data mesh.