Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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11 November 2018

How Upsolver Is Building A Data Lake Platform In The Cloud with Yoni Iny - Episode 56 - E56

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A data lake can be a highly valuable resource, as long as it is well built and well managed. Unfortunately, that can be a complex and time-consuming effort, requiring specialized knowledge and diverting resources from your primary business. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization.


  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to to get a $20 credit and launch a new server in under a minute.
  • Go to to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at
  • Your host is Tobias Macey and today I’m interviewing Yoni Iny about Upsolver, a data lake platform that lets developers integrate and analyze streaming data with ease


  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what Upsolver is and how it got started?
    • What are your goals for the platform?

  • There are a lot of opinions on both sides of the data lake argument. When is it the right choice for a data platform?

    • What are the shortcomings of a data lake architecture?

  • How is Upsolver architected?

    • How has that architecture changed over time?
    • How do you manage schema validation for incoming data?
    • What would you do differently if you were to start over today?

  • What are the biggest challenges at each of the major stages of the data lake?

  • What is the workflow for a user of Upsolver and how does it compare to a self-managed data lake?

  • When is Upsolver the wrong choice for an organization considering implementation of a data platform?

  • Is there a particular scale or level of data maturity for an organization at which they would be better served by moving management of their data lake in house?

  • What features or improvements do you have planned for the future of Upsolver?

Contact Info

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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