Designing And Building Data Platforms As A Product

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About this Episode


The term "data platform" gets thrown around a lot, but have you stopped to think about what it actually means for you and your organization? In this episode Lior Gavish, Lior Solomon, and Atul Gupte share their view of what it means to have a data platform, discuss their experiences building them at various companies, and provide advice on how to treat them like a software product. This is a valuable conversation about how to approach the work of selecting the tools that you use to power your data systems and considerations for how they can be woven together for a unified experience across your various stakeholders.


  • 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 Lior Gavish, Lior Solomon, and Atul Gupte about the technical, social, and architectural aspects of building your data platform as a product for your internal customers


  • Introduction
  • How did you get involved in the area of data management? – all
  • Can we start by establishing a definition of "data platform" for the purpose of this conversation?
  • Who are the stakeholders in a data platform?
    • Where does the responsibility lie for creating and maintaining ("owning") the platform?
  • What are some of the technical and organizational constraints that are likely to factor into the design and execution of the platform?
  • What are the minimum set of requirements necessary to qualify as a platform? (as opposed to a collection of discrete components)
    • What are the additional capabilities that should be in place to simplify the use and maintenance of the platform?
  • How are data platforms managed? Are they managed by technical teams, product managers, etc.? What is the profile for a data product manager? – Atul G.
  • How do you set SLIs / SLOs with your data platform team when you don’t have clear metrics you’re tracking? – Lior S.
  • There has been a lot of conversation recently about different interpretations of the "modern data stack". For a team who is just starting to build out their platform, how much credence should they be giving to those debates?
    • What are the first steps that you recommend for those practitioners?
    • If an organization already has infrastructure in place for data/analytics, how might they think about building or buying their way toward a well integrated platform?
  • Once a platform is established, what are some challenges that teams should anticipate in scaling the platform?
    • Which axes of scale have you found to be most difficult to manage? (scale of infrastructure capacity, scale of organizational/technical complexity, scale of usage, etc.)
    • Do we think the "data platform" is a skill set? How do we split up the role of the platform? Is there one for real-time? Is there one for ETLs?
    • How do you handle the quality and reliability of the data powering your solution?
  • What are helpful techniques that you have used for collecting, prioritizing, and managing feature requests?
    • How do you justify the budget and resources for your data platform?
    • How do you measure the success of a data platform?
  • What is the relationship between a data platform and data products?
  • Are there any other companies you admire when it comes to building robust, scalable data architecture?
  • What are the most interesting, innovative, or unexpected ways that you have seen data platforms used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while building and operating a data platform?
  • When is a data platform the wrong choice? (as opposed to buying an integrated solution, etc.)
  • What are the industry trends that you are monitoring/excited for in the space of data platforms?

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Parting Question

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


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

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