Data Observability Out Of The Box With Metaplane
Data Engineering Podcast
Do You Monitor the Causes or Do You Observe the Symptoms?
Yonote's metoplane aims to monitor the entire life cycle of data from transactional systems and sass platforms that originate the data through to delivery an interpretation. The highest leverage point for an observability tool is to connect to the warehouse, where that's the centre gravity of your data stack,. So we did start there. However, there's an ongoing to bay in observability, even in softer observability of do you monitor the causes, or do you monitoring the symptoms? There's prose and cons to both.
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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- 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
- @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.
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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
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