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.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro.
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- 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
- 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?
- 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 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.
- 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 email@example.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
- Anomstack Github repo
- Airflow Anomaly Detection Provider Github repo
- Metric Tree
- Semantic Layer
- Chaos Genius
- Anomstack Gallery