Digging Into Data Reliability Engineering


September 25th, 2021

58 mins 7 secs

Your Host

About this Episode


The accuracy and availability of data has become critically important to the day-to-day operation of businesses. Similar to the practice of site reliability engineering as a means of ensuring consistent uptime of web services, there has been a new trend of building data reliability engineering practices in companies that rely heavily on their data. In this episode Egor Gryaznov explains how this practice manifests from a technical and organizational perspective and how you can start adopting it in your own teams.


  • 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
  • Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
  • Your host is Tobias Macey and today I’m interviewing Egor Gryaznov, co-founder and CTO of Bigeye, about the ideas and practices of data reliability engineering and how to integrate it into your systems


  • Introduction
  • How did you get involved in the area of data management?
  • What does the term "Data Reliability Engineering" mean?
  • What is encompassed under the umbrella of Data Reliability Engineering?
    • How does it compare to the concepts from site reliability engineering?
    • Is DRE just a repackaged version of DataOps?
  • Why is Data Reliability Engineering particularly important now?
  • Who is responsible for the practice of DRE in an organization?
  • What are some areas of innovation that teams are focusing on to support a DRE practice?
  • What are the tools that teams are using to improve the reliability of their data operations?
  • What are the organizational systems that need to be in place to support a DRE practice?
    • What are some potential roadblocks that teams might have to address when planning and implementing a DRE strategy?
  • What are the most interesting, innovative, or unexpected approaches/solutions to DRE that you have seen?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Data Reliability Engineering?
  • Is Data Reliability Engineering ever the wrong choice?
  • What do you have planned for the future of Bigeye, especially in terms of Data Reliability Engineering?

Contact Info

  • Find us at bigeye.com or reach out to us at hello@bigeye.com
  • You can find Egor on LinkedIn or email him at egor@bigeye.com

Parting Question

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


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

Support Data Engineering Podcast