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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14 March 2022

Taking A Multidimensional Approach To Data Observability At Acceldata - E271

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Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into your running data platform and how they have architected their platform to assist in that endeavor.


  • 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 Tristan Spaulding about Acceldata, a platform offering multidimensional data observability for modern data infrastructure


  • Introduction
  • How did you get involved in the area of data?
  • Can you describe what Acceldata is and the story behind it?
  • What does it mean for a data observability platform to be "multidimensional"?
  • How do the architectural characteristics of the "modern data stack" influence the requirements and implementation of data observability strategies?
  • The data observability ecosystem has seen a lot of activity over the past ~2-3 years. What are the unique capabilities/use cases that Acceldata supports?
  • Who are your target users and how does that focus influence the way that you have approached feature and design priorities?
  • What are some of the ways that you are using the Acceldata platform to run Acceldata?
  • Can you describe how the Acceldata platform is implemented?
    • How have the design and goals of the system changed or evolved since you started working on it?
  • How are you managing the definition, collection, and correlation of events across stages of the data lifecycle?
  • What are some of the ways that performance data can feed back into the debugging and maintenance of an organization’s data ecosystem?
  • What are the challenges that data platform owners face when trying to interpret the metrics and events that are available in a system like Acceldata?
  • What are the most interesting, innovative, or unexpected ways that you have seen Acceldata used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Acceldata?
  • When is Acceldata the wrong choice?
  • What do you have planned for the future of Acceldata?

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

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