This Is How Observability Starts - With Modeling Pipeline Runs
In data platforms, it is easy to confuse business workflow state with job execution state.
A company moves through statuses like "analyzing collection," "saving results," or "generating reports." Those labels are useful for the product. They tell users and operators where the account sits in a longer process.
But they are a poor substitute for knowing whether a specific pipeline is pending, running, finished, or failed right now.
In this post, I will walk through why that gap matters, what we changed in a real project, and how treating pipeline runs as first-class processes improves both observability and control.
What we had before.
The platform processes data for multiple companies, each progressing through different stages and pipeline actions.
Before this change, pipeline execution was mostly inferred from company status transitions.
The API would get a trigger, enqueue a message, and a worker would pick up the job. When the...
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