Exploring Incident Management Strategies For Data Teams
Data Engineering Podcast
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Data Engineering - What Are the Differences to Indicator Management?
At my last company, ashe when though the process of having our platform as a batch process, datus, tak into real time streaming. It wasn't really able to focus alo ther productive time building y or real time streaming platform,. Building ar price experimentation platform. And eventually jump to the vendors side and devoting my life to building some onthe solutions. You mentioned that you've each had some experience working with customers and also, i'm sure, being on the operator side of these different types of data incidents. And i'm wondering if you can by describing some of the ways that an incident in the context of a data system might manifest.
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Summary
Data assets and the pipelines that create them have become critical production infrastructure for companies. This adds a requirement for reliability and management of up-time similar to application infrastructure. In this episode Francisco Alberini and Mei Tao share their insights on what incident management looks like for data platforms and the teams that support them.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Francisco Alberini and Mei Tao about patterns and practices for incident management in data teams
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by describing some of the ways that an "incident" can manifest in a data system?
- At a high level, what are the steps and participants required to bring an incident to resolution?
- The principle of incident management is familiar to application/site reliability teams. What is the current state of the art/adoption for these practices among data teams?
- What are the signals that teams should be monitoring to identify and alert on potential incidents?
- Alerting is a subjective and nuanced practice, regardless of the context. What are some useful practices that you have seen and enacted to reduce alert fatigue and provide useful context in the alerts that do get sent?
- Another aspect of this problem is the proper routing of alerts to ensure that the right person sees and acts on it. How have you seen teams deal with the challenge of delivering alerts to the right people?
- Alerting is a subjective and nuanced practice, regardless of the context. What are some useful practices that you have seen and enacted to reduce alert fatigue and provide useful context in the alerts that do get sent?
- When there is an active incident, what are the steps that you commonly see data teams take to understand the cause and scope of the issue?
- How can teams augment their systems to make incidents faster to resolve?
- What are the most interesting, innovative, or unexpected ways that you have seen teams approch incident response?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on incident management strategies?
- What are the aspects of incident management for data teams that are still missing?
Contact Info
- Mei
- Francisco
- @falberini 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.
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Links
- Monte Carlo
- Segment
- Segment Protocols
- Redshift
- Airflow
- dbt
- The Goal by Eliahu Golratt
- Data Mesh
- PagerDuty
- OpsGenie
- Grafana
- Prometheus
- Sentry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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