I designed an end-to-end observability workflow for an online retailer, then adapted it for an inventory prediction model to demonstrate how these strategies scale across different use cases to safely share. (And then wrote a post about it. đź“ť) The specific outcomes and tools matter less than the underlying approach.
The workflow begins when GitHub Actions triggers the CI/CD pipeline for testing and deployment. Once code is live, New Relic captures telemetry data while Dynatrace AI continuously analyzes it for anomalies—error spikes, latency issues, unexpected cloud spending, and similar problems. When issues arise, PagerDuty routes contextual alerts to the appropriate team members.
For critical failures, Shoreline automatically creates a new branch, commits the necessary fixes, and notifies authorized personnel to push changes to production. All incident data flows into BigQuery for long-term storage and analysis, then surfaces through Tableau dashboards where stakeholders can track resolution times, identify recurring patterns, and plan improvements. (New Relic also has elegant dashboards. If an organization is already established on a dashboard platform, like Looker, Power BI, or Tableau, it may make more sense to keep reporting there.)
I mapped this entire system in Miro, with detailed notes at each check point (indicated by the note icons in the bottom-left corners). The workflow leverages tools the organization either already uses or has committed to implementing, creating a streamlined DevOps process that reduces downtime and enables data-driven operational decisions across teams.