DataFusion Adoption Anti-Patterns After the POC

Many teams prove DataFusion value in a prototype but hit friction during production rollout.

The issue is rarely query capability. It is ownership and operational design.

Decision question

Should you expand DataFusion scope now, or constrain it to one execution lane until governance matures?

Anti-patterns

1. POC workloads are cleaner than real workloads Production data irregularities and edge-case semantics are underrepresented.
2. No explicit planner/executor ownership Changes accumulate without clear decision accountability.
3. No rollback design for execution regressions Teams discover release risk only after user-visible impact.
4. Observability added late Insufficient visibility into logical/physical plan behavior slows diagnosis.
5. Scope expansion before KPI proof Teams scale custom execution without evidence of better outcomes.

Recommended rollout model

• Select one high-impact workload as the first production lane.
• Define KPI targets and stop conditions before deployment.
• Build plan-level observability and rollback mechanisms up front.
• Expand only after two stable release cycles.

KPI target example

• 20% latency improvement on target workload
• no priority incidents during first two releases
• recovery time under 15 minutes for execution regressions

If your team is at this POC-to-production boundary, use a direct conversation with Stratorys to de-risk the transition.