Recovery objectives expose delays that ordinary availability metrics can hide.
Faster restart is valuable only when checkpoints, offsets, and output tables still agree.
Who you’d be doing this for
“The pipeline comes back eventually, but four hours of stale data is still an outage for us.”
Jan Wisniewski · Data Engineering Manager
Oversees streaming data products that operational teams depend on during infrastructure failures.
What is at stake
Only 73% of exercised stateful streaming pipelines recover within the four-hour objective. You have to weigh faster restoration against checkpoint and offset correctness.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- recovery speed vs consistency guarantees
- shared capacity vs critical-workload priority
- standardization vs workload-specific evidence
Why Databricks
Lakeflow supports streaming ETL workloads whose checkpoints and source offsets must recover without silent data loss or duplication.
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.