Data-Driven DevOps
summary
Presents a data-driven DevOps approach using three concrete metrics to start immediately: deployment frequency, pipeline lead time, and incident trend. Describes a reproducible data pipeline from Git, Jenkins, artifact stores and Jira, a bug audit that found only 20% of bugs required code changes (contrary to a 65% estimate), and how a remediation sprint saved 54 person-days; includes dashboards and custom Jira types to prove the changes and reduce wasted environments.
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chapters (13)
- 0:00Data-Driven DevOps intro
- 1:03Engineering overconfidence
- 5:23Missing project state
- 8:17Engineers isolated from data
- 12:35DevOps metrics abstract
- 16:28Data types and purposes
- 21:46Test stands utilization
- 24:34Test stability example
- 27:02Gaining team trust
- 29:10Data collection mistakes
- 33:55Real project analysis
- 36:48Data-Driven benefits
- 37:31Q&A
