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.

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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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Introduction to Data Mesh