Experimentation and A/B Testing For Modern Data Teams With Eppo - Episode 244

Summary

A/B testing and experimentation are the most reliable way to determine whether a change to your product will have the desired effect on your business. Unfortunately, being able to design, deploy, and validate experiments is a complex process that requires a mix of technical capacity and organizational involvement which is hard to come by. Chetan Sharma founded Eppo to provide a system that organizations of every scale can use to reduce the burden of managing experiments so that you can focus on improving your business. In this episode he digs into the technical, statistical, and design requirements for running effective experiments and how he has architected the Eppo platform to make the process more accessible to business and data professionals.

Datafold LogoDatafold is a data observability platform that helps companies prevent data catastrophes. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Datafold gives you visibility and confidence in the quality of your analytical data with fast dataset diffing, profiling, column-level lineage, and intelligent anomaly detection. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code.

Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

Atlan LogoHave you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?

Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. 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 and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.


Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!


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
  • Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
  • Your host is Tobias Macey and today I’m interviewing Chetan Sharma about Eppo, a platform for building A/B experiments that are easier to manage

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Eppo is and the story behind it?
  • What are some examples of the kinds of experiments that teams and organizations might want to conduct?
  • What are the points of friction that
  • What are the steps involved in designing, deploying, and analyzing the outcomes of an A/B experiment?
    • What are some of the statistical errors that are common when conducting an experiment?
  • What are the design and UX principles that you have focused on in Eppo to improve the workflow of building and analyzing experiments?
  • Can you describe the system design of the Eppo platform?
    • What are the services or capabilities external to Eppo that are required for it to be effective?
    • What are the integration points for adding Eppo to an organization’s existing platform?
  • Beyond the technical capabilities for running experiments there are a number of design requirements involved. Can you talk through some of the decisions that need to be made when deciding what to change and how to measure its impact?
  • Another difficult element of managing experiments is understanding how they all interact with each other when running a large number of simultaneous tests. How does Eppo help with tracking the various experiments and the cohorts that are bucketed into each?
  • What are some of the ideas or assumptions that you had about the technical and design aspects of running experiments that have been challenged or changed while building Eppo?
  • What are the most interesting, innovative, or unexpected ways that you have seen Eppo used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Eppo?
  • When is Eppo the wrong choice?
  • What do you have planned for the future of Eppo?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

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

Liked it? Take a second to support the Data Engineering Podcast on Patreon!