Exploring Incident Management Strategies For Data Teams
Show notes
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 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 RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! 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? Contact Info Mei @tao_mei on Twitter Email Francisco @falberini on Twitter Email 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. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Monte Carlo Learn more about RCA best practices Segment Podcast Episode Segment Protocols Redshift Airflow dbt Podcast Episode The Goal by Eliahu Golratt Data Mesh Podcast Episode Follow-Up Podcast Episode PagerDuty OpsGenie Grafana Prometheus Sentry Podcast.__init__ Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast
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