The Enterprise AI Show

The Enterprise AI Show explores the AI journey for Enterprise companies around the world. [formerly The Cloudcast]

As the AI revolution moves from experimentation to execution, The Enterprise AI Show provides the clarity needed to lead. Join Aaron Delp and Brian Gracely as they explore the intersection of generative AI, enterprise systems, and global business strategy. Each episode features clear-headed conversations with the people making actual decisions—founders, investors, and practitioners—focusing on the technical architectures and business models that drive real-world ROI.

New shows every Wednesday and Sunday.

Topics: Enterprise AI strategy · The AI Economy · LLMs in production · AI leadership · Agentic AI · Digital Sovereignty · Machine Learning · AI startups · Cloud Computing

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The Enterprise AI Show

Introduction to Data Mesh

July 22, 2020 • Cloudcast Media

0:00 | 44:14

Zhamak Dehghani (@zhamakd, Portfolio Tech Director @ThoughtWorks) talks about the concepts behind Data Mesh, the challenges and problems of Data Lakes / Data Warehouses, and how Cloud-native principles can be applied to Data.

SHOW: 459

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SHOW NOTES:

ThoughtWorks Technology Radar
Data Mesh (from Technology Radar)
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh

Topic 1 - Welcome to the show. We were introduced to you through the O’Reilly events, but you’ve been involved in software development and architecture for quite a while. Tell us a little bit about your background and your focus areas at ThoughtWorks.

Topic 2 - About a year ago, you introduced this new concept called “Data Mesh”. Before we get into that, give us a little bit of background on the problems that previous generations of Data Warehouses or Data Lakes created.

Topic 3 - Lets begin to walk through how Data Mesh is different from Data Lake. We’re not talking about just dumping all the various data sources into one “pool”, there’s a concept of “domains” within this big pool of data. What are the new concepts of source and consumption?

Topic 4 - Explain the concept of how pipelines are tied into Data Mesh and how this allows the creation of new products/features from the Data Mesh.

Topic 5 - You talk about the data being truthful, and then you bring an SRE concept of SLO into the truthfulness of the data. Explain how that might work?

Topic 6 - Once a Data Mesh is in place, what are the “roles” (or teams) that have specific tasks, and who are the typical consumers of the Data Mesh platform?

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