Summary

Data mesh is a frequent topic of conversation in the data community, with many debates about how and when to employ this architectural pattern. The team at AgileLab have first-hand experience helping large enterprise organizations evaluate and implement their own data mesh strategies. In this episode Paolo Platter shares the lessons they have learned in that process, the Data Mesh Boost platform that they have built to reduce some of the boilerplate required to make it successful, and some of the considerations to make when deciding if a data mesh is the right choice for you.

Announcements

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Your host is Tobias Macey and today I’m interviewing Paolo Platter about Agile Lab’s lessons learned through helping large enterprises establish their own data mesh

Interview

Introduction
How did you get involved in the area of data management?
Can you share your experiences working with data mesh implementations?
What were the stated goals of project engagements that led to data mesh implementations?
What are some examples of projects where you explored data mesh as an option and decided that it was a poor fit?
What are some of the technical and process investments that are necessary to support a mesh strategy?
When implementing a data mesh what are some of the common concerns/requirements for building and supporting data products?
- What are the general shape that a product will take in a mesh environment?
- What are the features that are necessary for a product to be an effective component in the mesh?
What are some of the aspects of a data product that are unique to a given implementation?
You built a platform for implementing data meshes. Can you describe the technical elements of that system?
- What were the primary goals that you were addressing when you decided to invest in building Data Mesh Boost?
How does Data Mesh Boost help in the implementation of a data mesh?
Code review is a common practice in construction and maintenance of software systems. How does that activity map to data systems/products?
What are some of the challenges that you have encountered around CI/CD for data products?
- What are the persistent pain points involved in supporting pre-production validation of changes to data products?
Beyond the initial work of building and deploying a data product there is the ongoing lifecycle management. How do you approach refactoring old data products to match updated practices/templates?
What are some of the indicators that tell you when an organization is at a level of sophistication that can support a data mesh approach?
What are the most interesting, innovative, or unexpected ways that you have seen Data Mesh Boost used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Data Mesh Boost?
When is Data Mesh (Boost) the wrong choice?
What do you have planned for the future of Data Mesh Boost?

Contact Info

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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