Summary
There has been a lot of discussion about the practical application of data mesh and how to implement it in an organization. Jean-Georges Perrin was tasked with designing a new data platform implementation at PayPal and wound up building a data mesh. In this episode he shares that journey and the combination of technical and organizational challenges that he encountered in the process.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender (https://www.dataengineeringpodcast.com/timextender) where you can do two things: watch us build a data estate in 15 minutes and start for free today.
Your host is Tobias Macey and today I'm interviewing Jean-Georges Perrin about his work at PayPal to implement a data mesh and the role of data contracts in making it work
Interview
Introduction
How did you get involved in the area of data management?
Can you start by describing the goals and scope of your work at PayPal to implement a data mesh?
What are the core problems that you were addressing with this project?
Is a data mesh ever "done"?
What was your experience engaging at the organizational level to identify the granularity and ownership of the data products that were needed in the initial iteration?
What was the impact of leading multiple teams on the design of how to implement communication/contracts throughout the mesh?
What are the technical systems that you are relying on to power the different data domains?
What is your philosophy on enforcing uniformity in technical systems vs. relying on interface definitions as the unit of consistency?
What are the biggest challenges (technical and procedural) that you have encountered during your implementation?
How are you managing visibility/auditability across the different data domains? (e.g. observability, data quality, etc.)
What are the most interesting, innovative, or unexpected ways that you have seen PayPal's data mesh used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh?
When is a data mesh the wrong choice?
What do you have planned for the future of your data mesh at PayPal?
Contact Info
LinkedIn (https://www.linkedin.com/in/jgperrin/)
Blog (https://jgp.ai/)
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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning.
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