Your pipelines. Observable. Self-healing. Finally.
A command-line orchestrator that turns tangled DAGs, broken cron jobs, and silent data failures into version-controlled pipelines that self-heal at 3 AM — so your engineers don't have to.
The data infrastructure crisis is real. Here are the numbers.
More time fixing pipelines than building them.
In a survey of 340 data engineers at companies with $10M+ ARR, nearly three quarters reported spending the majority of their on-call hours on pipeline maintenance rather than new feature development.
Pipeline watches every column type, nullable flag, and row count distribution. The moment upstream changes break your contract, the run pauses and your team gets an alert — before bad data reaches production.
Silent failures cost more than loud ones.
When a pipeline succeeds but produces wrong output — stale joins, dropped partitions, miscounted deduplication — the cost compounds invisibly for days or weeks before anyone notices.
When Pipeline detects a gap in your data — a missed partition, a failed window, a late-arriving event — it automatically schedules a dependency-aware backfill with no manual intervention.
The pager wakes the engineer. Not the other way around.
Transient upstream failures, network blips, and rate limits don't care about business hours. Most pipeline failures are recoverable — they just require someone to press retry at the worst possible time.
Pipeline knows the difference between a permanent failure and a transient one. It retries with exponential backoff, respects upstream availability windows, and only pages you when human judgment is actually needed.
Drop into your stack.
No rip-and-replace.
Pipeline runs alongside your existing tools — not instead of them. Instrument Airflow DAGs with three lines of config. Wrap dbt projects with zero schema changes. Connect Kafka topics without moving your consumers.
pipeline:
name: revenue_daily_v3
observe: airflow://revenue_etl_dag
sla: 30m
on_failure: auto_retry → backfill → alert
schema_contract: strict
lineage: auto
# That's it. Pipeline now observes, versions, and self-heals this DAG.The pager has been quiet
for the first time in months.
We had 47 Airflow DAGs, each maintained by a different person, none with consistent alerting. After wrapping them with Pipeline, we had full observability in a day. The schema drift detection alone caught three production incidents in the first week.
Your on-call rotation
deserves a break.
Start with your existing Airflow DAGs. Pipeline wraps them, observes them, and starts healing them — in under 10 minutes. No migration required.
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