CI/CD for Data Teams: Heavy Deps, Big Data, Long Jobs
Data CI is heavy and slow - big dependencies, large fixtures, and long-running validation jobs.
Data engineering teams run dbt, Airflow, and heavy Python in CI. Dependency weight, job length, and cost are the sharp edges.
Cache heavy dependencies
Cache pip/conda environments and prebuilt wheels so native data libraries do not recompile every run.
Handle big fixtures and artifacts
Large test datasets fill disk and slow uploads. Self-healing reclaims space and retries disk-full failures.
Right-size for memory
Data transforms OOM on small runners. Right-size memory; self-healing retries transient OOM and timeouts.
Cost on long jobs
Long validation jobs on premium runners are expensive. Roughly 69% savings plus recovered re-runs cuts the bill.
Key takeaways
- Cache heavy data deps.
- Right-size memory; self-heal OOM.
- About 69% cheaper on long jobs.
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