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