Digging Into Data Reliability Engineering
Data Engineering Podcast
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Monoring for Data Reliability - What Are the Tools That Are Needed?
Knowing what is actually going on in your system as being the first step. With data observability, the next step is then setting the expectations and communicating any issues that arise. Finally, there's the communication piece. Most tools provide a way for you to push notifications. Email has been the classic slack. I think the next step in the evolution of this notification is going to be something similar to a notification hub.
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Summary
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.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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
Interview
- 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?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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