Automated Data Quality Management Through Machine Learning With Anomalo
Data Engineering Podcast
Data Quality Monitoring - What You Have Learned?
We started as naively with this idea that we're going to have this great, amazing machine learning model that's going to solve everything. And it just wasn't enough. It wasn't something that out of the gate our customers could trust. The next key realization will how mature should they be? We did spend time working early on with companies that had selected the data warehouse, but actually weren't using it yet. That was one of the key tangs that we had to identify and understand.
Summary
Data quality control is a requirement for being able to trust the various reports and machine learning models that are relying on the information that you curate. Rules based systems are useful for validating known requirements, but with the scale and complexity of data in modern organizations it is impractical, and often impossible, to manually create rules for all potential errors. The team at Anomalo are building a machine learning powered platform for identifying and alerting on anomalous and invalid changes in your data so that you aren’t flying blind. In this episode founders Elliot Shmukler and Jeremy Stanley explain how they have architected the system to work with your data warehouse and let you know about the critical issues hiding in your data without overwhelming you with alerts.
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!
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses.
- Your host is Tobias Macey and today I’m interviewing Elliot Shmukler and Jeremy Stanley about Anomalo, a data quality platform aiming to automate issue detection with zero setup
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Anomalo is and the story behind it?
- Managing data quality is ostensibly about building trust in your data. What are the promises that data teams are able to make about the information in their control when they are using Anomalo?
- What are some of the claims that cannot be made unequivocally when relying on data quality monitoring systems?
- types of data quality issues identified
- utility of automated vs programmatic tests
- Can you describe how the Anomalo system is designed and implemented?
- How have the design and goals of the platform changed or evolved since you started working on it?
- What is your approach for validating changes to the business logic in your platform given the unpredictable nature of the system under test?
- model training/customization process
- statistical model
- seasonality/windowing
- CI/CD
- With any monitoring system the most challenging thing to do is avoid generating alerts that aren’t actionable or helpful. What is your strategy for helping your customers avoid alert fatigue?
- What are the most interesting, innovative, or unexpected ways that you have seen Anomalo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomalo?
- When is Anomalo the wrong choice?
- What do you have planned for the future of Anomalo?
Contact Info
- Elliot
- Jeremy
- @jeremystan 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
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
The AI-powered Podcast Player
