Summary

Data observability is a set of technical and organizational capabilities related to understanding how your data is being processed and used so that you can proactively identify and fix errors in your workflows. In this episode Metaplane founder Kevin Hu shares his working definition of the term and explains the work that he and his team are doing to cut down on the time to adoption for this new set of practices. He discusses the factors that influenced his decision to start with the data warehouse, the potential shortcomings of that approach, and where he plans to go from there. This is a great exploration of what it means to treat your data platform as a living system and apply state of the art engineering to it.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy.
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• Your host is Tobias Macey and today I’m interviewing Kevin Hu about Metaplane, a platform aiming to provide observability for modern data stacks, from warehouses to BI dashboards and everything in between.

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Metaplane is and the story behind it?
• Data observability is an area that has seen a huge amount of activity over the past couple of years. What is your working definition of that term?
  • What are the areas of differentiation that you see across vendors in the space?
• Can you describe how the Metaplane platform is architected?
  • How have the design and goals of Metaplane changed or evolved since you started working on it?
• establishing seasonality in data metrics
• blind spots from operating at the level of the data warehouse
• What are the most interesting, innovative, or unexpected ways that you have seen Metaplane used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Metaplane?
• When is Metaplane the wrong choice?
• What do you have planned for the future of Metaplane?

Contact Info

• LinkedIn
• @kevinzhenghu 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

• Metaplane
• Datadog
• Control Theory
• James Clerk Maxwell
• Centrifugal Governor
• Huygens
• Amazon ECS
• Stop Hiring Devops Experts (And Start Growing Them)

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