Summary

With all of the messaging about treating data as a product it is becoming difficult to know what that even means. Vishal Singh is the head of products at Starburst which means that he has to spend all of his time thinking and talking about the details of product thinking and its application to data. In this episode he shares his thoughts on the strategic and tactical elements of moving your work as a data professional from being task-oriented to being product-oriented and the long term improvements in your productivity that it provides.

Announcements

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Your host is Tobias Macey and today I'm interviewing Vishal Singh about his experience building data products at Starburst

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what your definition of a "data product" is?
- What are some of the different contexts in which the idea of a data product is applicable?
- How do the parameters of a data product change across those different contexts/consumers?
What are some of the ways that you see the conversation around the purpose and practice of building data products getting overloaded by conflicting objectives?
What do you see as common challenges in data teams around how to approach product thinking in their day-to-day work?
What are some of the tactical ways that product-oriented work on data problems differs from what has become common practice in data teams?
What are some of the features that you are building at Starburst that contribute to the efforts of data teams to build full-featured product experiences for their data?
What are the most interesting, innovative, or unexpected ways that you have seen Starburst used in the context of data products?
What are the most interesting, unexpected, or challenging lessons that you have learned while working at Starburst?
When is a data product the wrong choice?
What do you have planned for the future of support for data product development at Starburst?

Contact Info

LinkedIn
@vishal_singh on Twitter

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

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Using Product Driven Development To Improve The Productivity And Effectiveness Of Your Data Teams 58:46 Share

Data Engineering Podcast

Shownotes Transcript

Using Product Driven Development To Improve The Productivity And Effectiveness Of Your Data Teams