Build Trust In Your Data By Understanding Where It Comes From And How It Is Used With Stemma

Episode • Aug 10, 2021 • 52m

Summary

All of the fancy data platform tools and shiny dashboards that you use are pointless if the consumers of your analysis don’t have trust in the answers. Stemma helps you establish and maintain that trust by giving visibility into who is using what data, annotating the reports with useful context, and understanding who is responsible for keeping it up to date. In this episode Mark Grover explains what he is building at Stemma, how it expands on the success of the Amundsen project, and why trust is the most important asset for data teams.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Mark Grover about his work at Stemma to bring the Amundsen project to a wider audience and increase trust in their data.

Interview

Introduction
Can you describe what Stemma is and the story behind it?
Can you give me more context into how and why Stemma fits into the current data engineering world? Among the popular tools of today for data warehousing and other products that stitch data together – what is Stemma’s place? Where does it fit into the workflow?
How has the explosion in options for data cataloging and discovery influenced your thinking on the necessary feature set for that class of tools? How do you compare to your competitors
With how long we have been using data and building systems to analyze it, why do you think that trust in the results is still such a momentous problem?
Tell me more about Stemma and how it compares to Amundsen?
Can you tell me more about the impact of Stemma/Amundsen to companies that use it?
What are the opportunities for innovating on top of Stemma to help organizations streamline communication between data producers and consumers?
Beyond the technological capabilities of a data platform, the bigger question is usually the social/organizational patterns around data. How have the "best practices" around the people side of data changed in the recent past?
- What are the points of friction that you continue to see?
A majority of conversations around data catalogs and discovery are focused on analytical usage. How can these platforms be used in ML and AI workloads?
How has the data engineering world changed since you left Lyft/since we last spoke? How do you see it evolving in the future?
Imagine 5 years down the line and let’s say Stemma is a household name. How have data analysts’ lives improved? Data engineers? Data scientists?
What are the most interesting, innovative, or unexpected ways that you have seen Stemma used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Stemma?
When is Stemma the wrong choice?
What do you have planned for the future of Stemma?

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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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