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Control-M + AWS Data Pipeline
Your AWS Data Pipeline workflow starts before a pipeline run ever activates a single activity — and it can fail when something upstream breaks. An Amazon RDS snapshot export never lands in the expected Amazon S3 prefix. An Amazon EMR activity overruns its scheduled processing window. Control-M orchestrates the full chain: Amazon S3 data landing, Amazon EMR transformation, AWS Data Pipeline execution, and Amazon Redshift delivery.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running AWS Data Pipeline workflows across multiple tools. Here’s how Control‑M handles each one.
UPSTREAM DELAYS
Your S3 data landed late. The pipeline already started.
Control-M validates upstream dependencies before launching AWS Data Pipeline. Instead of relying on fixed schedules, it waits for confirmed file arrival, API completion, or job success before triggering execution, preventing failed runs and unnecessary reprocessing.
FAILED ACTIVITIES
One activity failed overnight. Everything downstream kept waiting.
Control-M detects pipeline failures immediately, applies configurable retry policies, prevents downstream execution, and alerts the right teams. Once recovery conditions are met, orchestration resumes automatically without restarting the entire workflow or manual intervention.
CROSS-CLOUD DEPENDENCIES
Your Spark job finished. The next AWS pipeline never triggered.
Control-M orchestrates dependencies across Spark, databases, cloud storage, ETL platforms, and AWS Data Pipeline using event-driven automation instead of disconnected schedulers. Every workload advances only after prerequisite conditions have been successfully satisfied.
SLA VISIBILITY
The morning dashboard is due. Nobody knows the pipeline status.
Control-M provides centralized monitoring, SLA tracking, and predictive alerts across the complete workflow. Operations teams can identify delays before business deadlines are missed and take action from a single orchestration interface.
MANUAL RECOVERY
Yesterday's failed run still needs manual cleanup before rerunning.
Control-M automates recovery workflows by executing remediation steps, validating dependencies, and restarting only the affected portions of the workflow. This reduces operational effort, shortens recovery time, and keeps downstream processing consistent.
INTEGRATION FACTS
Control‑M + AWS Data Pipeline
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workload.types |
ETL workflows · data movement · Amazon EMR processing · Amazon EC2 activities · SQL execution · data transformation · scheduled batch pipelines |
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trigger.type |
time schedule · file arrival (Amazon S3) · upstream job completion · REST API call · event-driven workflow · manual trigger · job exit status |
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cross_tool.deps |
Amazon S3 · Amazon EMR · Amazon EC2 · AWS Lambda · Amazon RDS · REST APIs · file transfer workflows |
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cloud.platforms |
AWS · hybrid cloud · on-premises environments · Control-M SaaS · Control-M self-hosted |
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error_handling |
configurable retry policies · exit-state detection · downstream dependency control · automated recovery workflows · SLA breach alerts · PagerDuty integration · Communication Suite alerts (Teams, Slack, Telegram, WhatsApp) |
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throughput |
batch processing up to 50 simultaneous jobs per Agent · large-scale ETL workloads · multi-stage data pipelines · parallel workflow orchestration · high-volume data movement |
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observability |
centralized workflow monitoring · job-level audit logs · dependency lineage · SLA tracking with prediction · unified operations dashboard |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across AWS Data Pipeline, Amazon S3, Amazon EMR, AWS Lambda, Amazon RDS, file transfers, REST APIs, and cloud services in a single job flow—with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: S3 file arrival → AWS Data Pipeline → Amazon EMR → Amazon RDS → downstream analytics
- Data-aware triggers: file arrival, API event, upstream job completion, pipeline completion
|
AWS Data Pipeline |
pipeline scheduling · execution orchestration · dependency management · status monitoring · automated recovery |
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Amazon S3 |
file arrival detection · event-based triggers · data availability validation · secure file orchestration |
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Amazon EMR |
cluster job orchestration · dependency control · workload sequencing · completion monitoring |
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AWS Lambda |
function invocation · event-driven automation · downstream workflow triggering · execution tracking |
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Amazon RDS |
SQL execution · database dependency orchestration · post-processing workflows · completion validation |
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REST APIs |
API invocation · response validation · conditional workflow execution · external system integration |
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File Transfer Workflows |
managed file transfers · delivery confirmation · checksum validation · downstream automation |
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 issues 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 a data pipelines
- Python operators, sensors, and task dependencies
- Execution graphic 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 ever starts
- Existing DAGs don’t need to be rewritten or migrated
MONITOR BACKUPS
Monitor AWS Data Pipeline execution from one operational view.
AWS Data Pipeline provides pipeline status, but it doesn't provide unified visibility across upstream systems, downstream applications, and external dependencies. Control-M centralizes monitoring across the entire workflow, giving operations teams complete execution context through:
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End-to-end workflow visibility
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Pipeline execution status
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Runtime and duration history
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Dependency and lineage tracking
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SLA risk monitoring
SLA ASSURANCE
Keep AWS Data Pipeline workloads on schedule every day.
Meeting business deadlines depends on far more than the pipeline itself. Control-M continuously monitors dependencies, predicts SLA risks, automates recovery, and alerts operators before delays impact downstream analytics, reporting, or business operations through:
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Predictive SLA monitoring
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Automated failure recovery
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Configurable retry policies
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Proactive operator alerts
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Downstream dependency protection
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
