Let us know how we can help
Sales & Pricing
Speak to a rep about your business needs
Help & Support
See our product support options
General inquiries and locations
Contact us
Control-M + Amazon SageMaker
Your Amazon SageMaker workflow starts before a SageMaker pipeline execution ever runs — and it can fail when something upstream breaks. An Amazon S3 training dataset that arrives late or incomplete. An AWS Glue transformation job that misses its processing window. Control-M orchestrates the full chain: Amazon S3 data arrival, AWS Glue transformation, SageMaker pipeline execution, and downstream analytics 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 DataSync transfers across multiple tools. Here’s how Control‑M handles each one.
UPSTREAM DATA
Your S3 training data is late. SageMaker must not start.
Control-M holds the SageMaker pipeline behind the required upstream dependencies and releases execution only when those conditions are satisfied. The ML workflow stays coordinated with the data pipeline instead of starting from an independent schedule.
PIPELINE FAILURE
Your SageMaker pipeline failed. The downstream workflow is still waiting.
Control-M monitors SageMaker pipeline status, results, and output, then applies workflow logic when execution fails. Downstream dependencies remain controlled while operators get the execution context needed to recover the pipeline without blindly advancing the workflow.
RETRY CONTROL
A SageMaker pipeline stopped. You need a controlled retry.
Control-M can retry a previous SageMaker pipeline execution using its Pipeline Execution ARN, with configurable status polling and failure tolerance. Recovery stays inside the orchestrated workflow instead of depending on an operator manually restarting execution in AWS.
CROSS-TOOL DEPENDENCIES
Your AWS Glue job finished. SageMaker needs to run next.
Control-M places AWS Glue processing and SageMaker execution in the same end-to-end workflow, using dependencies to coordinate the handoff. Teams can manage the sequence across services without maintaining separate schedules or custom cross-platform trigger logic.
SLA RISK
Training is still running. Your 7:00 AM analytics handoff is at risk.
Control-M attaches SLA management to the SageMaker job and tracks it within the broader production workflow. Teams gain visibility into timing risk before the downstream delivery window is missed, rather than discovering the delay after consumers are already waiting.
Control‑M + Amazon SageMaker
Control‑M + Amazon SageMaker
|
workload.types |
SageMaker Pipelines · pipeline execution · parameterized pipeline runs · pipeline execution retry · ML training workflows · model processing workflows |
|
trigger.type |
time schedule · upstream job completion · file arrival · API-driven execution · Control-M dependency · business calendar · resource availability |
|
cross_tool.deps |
Amazon S3 data arrival · AWS Glue job · Apache Airflow DAG · AWS Step Functions workflow · file transfer completion · database job · downstream analytics handoff |
|
cloud.platforms |
AWS · Amazon SageMaker regional endpoints · Control-M SaaS · Control-M self-managed |
|
error_handling |
pipeline execution retry · Pipeline Execution ARN · status polling frequency · failure tolerance · dependency-based cascade prevention · job status monitoring · workflow recovery |
|
throughput |
Up to 50 SageMaker jobs simultaneously per Agent (recommended guideline) · parallel pipeline execution · resource pools · scheduling controls · scalable enterprise workflow coordination |
|
observability |
job status · pipeline results · job output · SLA jobs · end-to-end workflow monitoring · dependency visibility · execution history |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Amazon SageMaker, Amazon S3, AWS Glue, Airflow, AWS Step Functions, file transfers, and analytics services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: AWS Glue → Amazon SageMaker pipeline → model output → analytics handoff
- Data-aware triggers: S3 file arrival, API event, upstream job completion, pipeline result
|
Amazon SageMaker |
execute pipelines · pass pipeline parameters · retry pipeline execution · monitor status and output · attach SLA jobs |
|
Amazon S3 |
coordinate data arrival · gate downstream processing · sequence training-data dependencies |
|
AWS Glue |
orchestrate data preparation · monitor job completion · trigger downstream ML workflows Apache Airflow → trigger DAGs · monitor execution · coordinate DAG dependencies with enterprise workflows |
|
AWS Step Functions |
execute workflows · monitor status · coordinate application and ML dependencies |
|
File Transfer |
coordinate file delivery · gate pipeline execution · connect external data sources |
|
Analytics Services |
coordinate downstream delivery · sequence model outputs · protect reporting dependencies |
AIRFLOW COEXISTENCE
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 SageMaker execution in the full data pipeline
SageMaker shows what happens inside your ML environment; production dependencies often extend well beyond it. Control-M monitors SageMaker status, results, and output alongside the jobs that supply and consume its data, giving teams one operational view of:
-
SageMaker pipeline execution status
-
Job results and output
-
Upstream and downstream dependencies
-
End-to-end workflow status
SLA ASSURANCE
Keep SageMaker pipelines aligned with delivery SLAs
A successful SageMaker execution can still be late for the business process that depends on it. Control-M adds SLA management to SageMaker jobs and coordinates timing across the surrounding workflow, helping teams identify and manage delivery risk across:
-
End-to-end SLA tracking
-
Cross-platform dependency timing
-
Pipeline status and results
-
Downstream delivery coordination
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
