Data Mesh Resource Center

A Paradigm Shift in Data & Analytics

Enjoy exclusive access to Data Mesh content, including on-demand talks, panel discussions featuring Zhamak Dehghani, Founder of Data Mesh, and more!

Starburst Data Mesh Solution Brief

What is Data Mesh?

Data Mesh – an approach founded by Zhamak Dehghani – refers to a decentralized, distributed approach to enterprise data management. It is a holistic concept that sees different datasets as distributed products, orientated around domains. The idea is that each domain-specific dataset has its own embedded engineers and product owners to manage that data and its availability to other teams, driving a level of data ownership and responsibility, which is often lacking in the current data platforms that are largely centralised, monolithic, and often built around complex pipelines.

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Data Mesh is founded around four core principles:

Domain-driven data ownership architecture

Data as a product

Self-serve infrastructure as a platform

Federated computational governance

“Data Mesh is not just an architecture, it’s not just a technology, it’s not just an organizational change, it’s all of the above. It’s a paradigm shift in how we manage big data between planes.”

– Zhamak Dehghani, Founder of Data Mesh

Introduction to Data Mesh

In this introduction to Data Mesh, Zhamak Dehghani gives a high-level overview of the concept, which is a paradigm shift that draws from modern distributed architecture considering domains as the first-class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.

How to Build a Foundation for Data Mesh: A Principled Approach

In this session, Zhamak Dehghani gives an in-depth explanation of how to apply Data Mesh to your current architecture, following four principles: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance.

What Data Mesh Means for Data Analysts

What does data mesh actually mean for data analysts? In this panel discussion moderated by moderated by Sean Zinsmeister, VP Product Marketing at ThoughtSpot, panelists Zhamak Dehghani, founder of the term Data Mesh, Daniel Abadi, Darnell-Kanal Professor of Computer Science at University of Maryland, College Park, and Gareth Stevenson, Director, Senior Quantitative Analyst, Bank of America share an interesting mix of perspectives.

More Resources

SQL is Your Data Mesh API

This blog describes why an externalized query service, like Starburst, and a data access control tool, like Immuta provide the perfect framework to enable data mesh.

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Data Mesh: The Answer to the Data Warehouse Hypocrisy

Computer Science Professor Daniel Abadi shares his perspective on the Data Mesh and it’s effectiveness and application in relation to the Data Warehouse architecture.

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Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes Beyond the Data Lake

Check out Data Mesh in practice in this talk from the Databricks AI Summit 2020 which follows our customer Zalando’s journey from a centralized Data Lake to a distributed Data Mesh architecture backed by Spark and built on Delta Lake.

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Why Governance is the Critical Stitch in Data Mesh (and How to Avoid “Meshing” it Up)

In this blog, Rajiv Dholakia, Privacera VP of Product, and Matt Fuller, Starburst Co-Founder and VP of Product explain how Starburst Stargate enables cross-cloud analytics on data distributed globally to helps bridge the gap to the new paradigm of Data Mesh, while leveraging data access governance partners like Privacera to ensure regulatory compliance is not compromised at the expense of enabling rapid access to analytics to drive business initiatives.

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Starburst Backs Data Mesh Architecture

In this article, Starburst’s Chairman, CEO and Co-Founder Justin Borgman shares his perspective on Data Mesh and explains why Starburst is ‘The Analytics Engine for Data Mesh.’

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