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
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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?
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?
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.
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