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Control-M + AWS Step Functions
Your AWS Step Functions workflow starts before a state machine execution ever invokes a single state — and it can fail when something upstream breaks. An Amazon SQS message never arrives in the expected input queue. An AWS Lambda function exceeds its execution timeout before writing its payload to Amazon S3. Control-M orchestrates the full chain: Amazon SQS message arrival, AWS Lambda transformation, AWS Step Functions execution, and Amazon DynamoDB 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 DEPENDENCIES
Your Glue job failed. The state machine still started.
AWS Step Functions executes when invoked, but it doesn't validate upstream dependencies across your broader workflow. Control-M verifies prerequisite job completion, evaluates execution status, and triggers the state machine only when every dependency has successfully completed, preventing downstream failures.
FAILURE RECOVERY
A Lambda retry succeeded. Your downstream pipeline never resumed.
Native retries resolve individual task failures but don't coordinate recovery across connected platforms. Control-M detects successful recovery, resumes dependent workflows automatically, and prevents duplicate processing or manual intervention while maintaining end-to-end workflow integrity
CROSS-PLATFORM FLOWS
Your state machine finished. Redshift and Snowflake are still waiting.
AWS Step Functions completes its execution, but downstream analytics platforms still require orchestration. Control-M detects execution completion, validates post-processing conditions, and automatically launches dependent jobs across data warehouses, ETL tools, and enterprise applications without custom glue code.
SLA VISIBILITY
Everything is running. Nobody knows you're about to miss the SLA.
Individual services expose execution status, but they don't predict business-level delivery risk. Control-M monitors the complete workflow, forecasts SLA breaches before they occur, alerts operations teams, and enables proactive intervention before downstream consumers are affected.
EVENT COORDINATION
The S3 file arrived late. Your overnight workflow missed its window.
Event-driven architectures still require coordinated execution across multiple systems. Control-M combines file events, API calls, schedules, and application completions into a single orchestrated workflow, automatically adjusting execution order while preserving dependencies and meeting production SLAs.
INTEGRATION FACTS
Control‑M + AWS Step Functions
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workload.types |
Standard Workflows · Express Workflows · state machine executions · serverless data pipelines · ETL orchestration · event-driven workflows · long-running business processes · distributed microservices |
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trigger.type |
API StartExecution request · Amazon S3 object creation · time schedule · upstream job completion · REST API call · Control-M workflow dependency |
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cross_tool.deps |
AWS Glue job completion · AWS Lambda invocation · Amazon EMR workflow · Amazon ECS/Fargate task · AWS Batch job · Amazon Redshift data load · Amazon S3 file delivery · REST API integration |
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cloud.platforms |
Amazon Web Services (AWS) · Control-M SaaS · Control-M on-premises · hybrid cloud environments · multi-cloud orchestration |
|
error_handling |
configurable retry count · execution failure detection · timeout handling · downstream cascade prevention · automated job hold on upstream failure · SLA pre-breach alert · PagerDuty integration · Communication Suite alerts (Teams, Slack, Telegram, WhatsApp) |
|
throughput |
workflow orchestration up to 50 simultaneous jobs per Agent · Standard Workflows · Express Workflows · event-driven execution · parallel state processing · large-scale serverless automation |
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observability |
execution status monitoring · job-level audit log · dependency lineage graph · SLA tracking with breach prediction · centralized workflow dashboard · Datadog integration |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across AWS Step Functions, AWS Glue, AWS Lambda, Amazon EMR, Amazon S3, Amazon Redshift, file transfers, and enterprise applications in a single job flow—with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: S3 file arrival → AWS Glue ETL → AWS Step Functions → AWS Lambda → Amazon Redshift load → downstream analytics
- Data-aware triggers: S3 object creation, API request, upstream job completion, Step Functions execution status
|
AWS Step Functions |
Trigger state machine executions · monitor execution status · manage workflow dependencies · coordinate downstream processing |
|
AWS Glue |
Trigger ETL jobs · monitor job completion · validate execution results · orchestrate downstream workflows |
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AWS Lambda |
Invoke serverless functions · coordinate event-driven execution · monitor outcomes · manage retries |
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Amazon EMR |
Launch Spark workloads · monitor cluster jobs · synchronize big data processing · manage dependencies |
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Amazon S3 |
File arrival detection · event-based workflow triggering · data availability validation · managed file dependencies |
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Amazon Redshift |
Trigger data loads · coordinate warehouse refreshes · validate load completion · launch downstream analytics |
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 WORKFLOWS
Monitor AWS Step Functions executions in one place.
AWS Step Functions provides visibility into individual state machine executions, but it doesn't provide a unified operational view across upstream and downstream systems. Control-M centralizes workflow monitoring across your entire production pipeline, giving operations teams complete visibility into execution health, dependencies, and delivery status:
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End-to-end workflow status
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State machine execution history
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Cross-platform dependencies
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Runtime and duration metrics
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Centralized operational dashboard
SLA ASSURANCE
Keep AWS Step Functions workflows on schedule.
AWS Step Functions manages workflow execution but doesn't track business SLAs across the broader production process. Control-M continuously monitors workflow progress, predicts SLA risks before deadlines are missed, and automates recovery actions to keep critical data pipelines and business processes on track:
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SLA breach prediction
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Automated failure recovery
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Intelligent alerting
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Dependency-aware scheduling
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Business service dashboards
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
