Summary

The data mesh is a thesis that was presented to address the technical and organizational challenges that businesses face in managing their analytical workflows at scale. Zhamak Dehghani introduced the concepts behind this architectural patterns in 2019, and since then it has been gaining popularity with many companies adopting some version of it in their systems. In this episode Zhamak re-joins the show to discuss the real world benefits that have been seen, the lessons that she has learned while working with her clients and the community, and her vision for the future of the data mesh.

Announcements

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Your host is Tobias Macey and today I’m welcoming back Zhamak Dehghani to talk about her work on the data mesh book and the lessons learned over the past 2 years

Interview

Introduction
How did you get involved in the area of data management?
Can you start by giving a brief recap of the principles of the data mesh and the story behind it?
How has your view of the principles of the data mesh changed since our conversation in July of 2019?
What are some of the ways that your work on the data mesh book influenced your thinking on the practical elements of implementing a data mesh?
What do you view as the as-yet-unknown elements of the technical and social design constructs that are needed for a sustainable data mesh implementation?
In the opening of your book you state that "Data Mesh is a new approach in sourcing, managing, and accessing data for analytical use cases at scale". As with everything, scale is subjective, but what are some of the heuristics that you rely on for determining when a data mesh is an appropriate solution?
What are some of the ways that data mesh concepts manifest at the boundaries of organizations?
While the idea of federated access to data product quanta reduces the amount of coordination necessary at the organizational level, it raises the spectre of more complex logic required for consumers of multiple quanta. How can data mesh implementations mitigate the impact of this problem?
What are some of the technical components that you have found to be best suited to the implementation of data elements within a mesh?
What are the technological components that are still missing for a mesh-native data platform?
How should an organization that wishes to implement a mesh style architecture think about the roles and skills that they will need on staff?
- How can vendors factor into the solution?
What is the role of application developers in a data mesh ecosystem and how do they need to change their thinking around the interfaces that they provide in their products?
What are the most interesting, innovative, or unexpected ways that you have seen data mesh principles used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh implementations?
When is a data mesh the wrong approach?
What do you think the future of the data mesh will look like?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Data Engineering Podcast Data Mesh Interview
Data Mesh Book
Thoughtworks
Expert Systems
OpenLineage
- Podcast Episode
Data Mesh Learning

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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