Data Observability Out Of The Box With Metaplane
Data Engineering Podcast
Data Observability
Lok: Data is a hot space, and that many people are trying to k into it. But there's kind of disconnect between junior and senior. And the way to bridge that gap in lieu of external training is being able to foster that talent yourselves. Lok: Maybe to unlock oot, you have to do one time, just a yes. Yes, but then you don't get to have the joy of configuring your o d b, c drivers. That's fisser. It's never flavy, not even the first time. You're right about that. The data ecosystem is still very nascent. So definitely apreciate all the time and energy you've put
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
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- 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.
- Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch.
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
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
Remember Everything You Learn from Podcasts
