Pipeline start time can be dominated by work that happens before data moves.
A narrowly scoped optimization still has to preserve the ordering guarantees users depend on.
Who you’d be doing this for
“Our data is ready to run, but the pipeline sits there planning for minutes.”
Mahlet Rwigamba · Data Engineer
Maintains a reporting pipeline whose materialized views refresh before the morning analytics window.
What is at stake
P95 planning time rose from 31 seconds to 148 seconds after a release. You have to weigh a focused performance fix against preserving dependency order.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- planning speed vs dependency correctness
- minimal patch vs durable diagnosis
- automated evidence vs code-level verification
Why Databricks
Delta Pipelines runs declarative ETL workloads at a scale where planning inefficiency delays customer data operations.
Written with these in mind
Not your kind of problem? 34 more at Databricks, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.