Summary

If your business metrics looked weird tomorrow, would you know about it first? Anomaly detection is focused on identifying those outliers for you, so that you are the first to know when a business critical dashboard isn't right. Unfortunately, it can often be complex or expensive to incorporate anomaly detection into your data platform. Andrew Maguire got tired of solving that problem for each of the different roles he has ended up in, so he created the open source Anomstack project. In this episode he shares what it is, how it works, and how you can start using it today to get notified when the critical metrics in your business aren't quite right.

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 Andrew Maguire about his work on the Anomstack project and how you can use it to run your own anomaly detection for your metrics

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Anomstack is and the story behind it?
- What are your goals for this project?
- What other tools/products might teams be evaluating while they consider Anomstack?
In the context of Anomstack, what constitutes a "metric"?
- What are some examples of useful metrics that a data team might want to monitor?
You put in a lot of work to make Anomstack as easy as possible to get started with. How did this focus on ease of adoption influence the way that you approached the overall design of the project?
What are the core capabilities and constraints that you selected to provide the focus and architecture of the project?
Can you describe how Anomstack is implemented?
- How have the design and goals of the project changed since you first started working on it?
What are the steps to getting Anomstack running and integrated as part of the operational fabric of a data platform?
- What are the sharp edges that are still present in the system?
What are the interfaces that are available for teams to customize or enhance the capabilities of Anomstack?
What are the most interesting, innovative, or unexpected ways that you have seen Anomstack used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomstack?
When is Anomstack the wrong choice?
What do you have planned for the future of Anomstack?

Contact Info

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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links

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

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Episode description

Run Your Own Anomaly Detection For Your Critical Business Metrics With Anomstack

Episode description