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Control-M + Airbyte
Your Airbyte workflow starts before a connection sync ever executes — and it can fail when something upstream breaks. A PostgreSQL source extract isn’t ready. A dbt Cloud transformation misses its expected completion window. Control-M orchestrates the full chain: source data arrival, upstream transformation, Airbyte sync execution, and downstream delivery.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running Airbyte syncs across multiple tools. Here’s how Control-M handles each one.
SOURCE READINESS
PostgreSQL isn’t ready. Your Airbyte sync is already starting.
Control-M holds the Airbyte job until upstream prerequisites complete, then executes the required connection sync. Instead of relying on disconnected schedules, data engineers can make source readiness an explicit dependency before replication begins.
CROSS-TOOL FLOW
Airbyte finished successfully. Your dbt transformation is still waiting.
Control-M monitors the Airbyte job status and releases downstream work when execution completes successfully. Airbyte, dbt, warehouse processing, and analytics delivery can progress as one dependency-driven workflow instead of separate schedules and manual handoffs.
FAILED SYNC
The 2:15 AM Airbyte sync failed. Downstream jobs kept moving.
Control-M tracks Airbyte job status using configurable polling and failure-tolerance settings, surfaces unsuccessful execution, and prevents dependent jobs from progressing incorrectly. Teams see the failed step in the context of the complete production workflow.
RESET CONTROL
You need a reset. Everything downstream must wait for it.
Control-M can execute Airbyte Re-Sync jobs for a specified Connection ID and coordinate the dependent workflow around them.. Downstream transformations and reporting remain gated until the reset and subsequent synchronization reach the required completion state.
SLA RISK
The Airbyte sync is running. Your 7:00 AM dashboard is at risk.
Control-M connects the Airbyte job to the end-to-end service SLA, monitors workflow progress, and identifies potential delays before the business deadline. Teams can act on pipeline risk instead of discovering it after analytics delivery is late.
Control‑M + Airbyte
Control‑M + Airbyte
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workload.types |
connection sync · Re-Sync (full data retrieval) · incremental replication* · full-refresh replication* · source-to-destination data movement |
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trigger.type |
advanced scheduling criteria · upstream job completion · file arrival event · API event · Control-M event · time schedule |
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cross_tool.deps |
Apache Airflow DAG · dbt transformation · Snowflake job · Databricks job · managed file transfer · REST API call |
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cloud.platforms |
Airbyte Cloud · self-managed Airbyte endpoint · AWS workflows · Microsoft Azure workflows · Google Cloud workflows |
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error_handling |
status polling frequency · failure tolerance · downstream cascade prevention · Control-M alerts · SLA Management · controlled rerun |
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throughput |
connection-level orchestration · incremental replication* · full-refresh replication* · configurable status polling · no per-record orchestration |
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observability |
Airbyte job status · results and output · optional REST response output · Control-M job history · dependency visibility · SLA monitoring |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Airbyte, Airflow, dbt, Snowflake, Databricks, file transfers, and cloud services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: source extract → Airbyte sync → dbt transformation → analytics handoff
- Data-aware triggers: file arrival, API event, upstream completion, Airbyte sync completion
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Airbyte |
execute Sync · execute Reset · monitor status · capture results/output · attach SLA |
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Apache Airflow |
trigger DAG · monitor execution · coordinate upstream/downstream dependencies |
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dbt |
trigger transformation · sequence after ingestion · coordinate downstream dependencies |
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Snowflake |
orchestrate warehouse jobs · sequence transformations · coordinate analytics delivery |
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Databricks |
orchestrate processing jobs · dependency control · end-to-end monitoring |
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Managed File Transfer |
file arrival dependency · secure transfer · delivery-driven workflow progression |
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Cloud services |
cross-cloud dependencies · API-driven execution · unified workflow monitoring |
airflow coexistance
Control-M doesn’t replace your Airflow DAGs. It runs the layer above them.
The objection is common: “We’re already on Airflow.” The issue isn’t what Airflow does – it’s what happens before and after Airflow runs. That’s where pipelines actually fail.
Airflow manages its DAG. Control-M manages everything surrounding it.
AIRFLOW HANDLES
DAG-level orchestration inside the data pipeline
- DAG-level task orchestration within data pipelines
- Python operators, sensors, and task dependencies
- Execution graph for jobs that run inside your pipeline
- Manages retries within a single DAG context
control-m adds
The coordination layer around your DAGs
- Coordination layer around DAGs — triggers Airflow based on upstream conditions: file arrivals, API events, other tool completions
- Tracks each DAG’s SLA contribution across the full end-to-end workflow, not just its own routine
- Manages failure recovery when upstream dependencies fail before Airflow even starts
- Existing DAGs don’t need to be rewritten or migrated
MONITOR PIPELINES
Monitor Airbyte syncs inside the complete data workflow.
Airbyte shows what happened inside its connection; the operational question is what that result means to everything around it. Control-M combines Airbyte execution with upstream and downstream workflow status so data teams can monitor production delivery in context:
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Airbyte job status
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Sync results and output
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Cross-platform dependency status
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Workflow execution history
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Downstream impact visibility
SLA ASSURANCE
Protect the SLA beyond the Airbyte sync.
A successful Airbyte sync does not guarantee the business pipeline will finish on time. Control-M measures Airbyte within the complete service path, identifies workflow delays, and enables intervention before downstream analytics or reporting misses its committed delivery time:
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End-to-end SLA monitoring
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Critical-path delay visibility
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Predicted late-service alerts
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Downstream impact analysis
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Service-level execution history
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
