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Control-M + AWS Glue DataBrew
Your AWS Glue DataBrew workflow starts before a job run ever applies a recipe to a dataset — and it can fail when something upstream breaks. An AWS Database Migration Service task never writes its extracted records to the source Amazon S3 prefix. An AWS Glue crawler overruns its scheduled window, leaving the Data Catalog table stale. Control-M orchestrates the full chain: Amazon S3 data landing, AWS Glue Data Catalog registration, AWS Glue DataBrew 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 Glue DataBrew jobs across multiple tools. Here’s how Control‑M handles each one.
UPSTREAM DELAYS
Your DataBrew job starts. The source dataset still isn't ready.
DataBrew depends on clean, available data before recipes can run. Control-M validates upstream dependencies—including file arrivals, Glue crawlers, ETL jobs, and database loads—before launching DataBrew, preventing failed executions and unnecessary reruns.
FAILED PREPARATION
A recipe failed overnight. Downstream analytics kept running anyway.
Control-M detects DataBrew job exit states immediately, stops dependent workflows from executing on incomplete or invalid data, triggers configurable retries or remediation workflows, and resumes downstream processing only after successful completion.
CROSS-SERVICE FLOWS
DataBrew finished. Glue, Athena, and Redshift never received the handoff.
Control-M orchestrates dependencies across AWS services and external platforms, automatically triggering downstream Glue jobs, Athena queries, Amazon Redshift loads, APIs, or third-party tools without polling, custom scripting, or manual intervention.
SLA PRESSURE
The morning dashboard deadline is approaching. Nobody knows what's delayed.
Control-M continuously tracks workflow progress across the entire pipeline, predicts SLA breaches before they occur, alerts the right teams, and provides end-to-end visibility so issues can be resolved before business users are affected.
MANUAL RECOVERY
One failed DataBrew job triggered hours of manual investigation.
Instead of restarting jobs manually and checking dependencies one by one, Control-M automates recovery using configurable retry policies, conditional logic, notifications, and dependency-aware restart capabilities, dramatically reducing operational effort and recovery time.
INTEGRATION FACTS
Control‑M + AWS Glue DataBrew
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workload.types |
Data preparation recipes · profile jobs · data quality validation · dataset transformations · schema discovery · batch data preparation · scheduled DataBrew jobs |
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trigger.type |
Amazon S3 file arrival · time schedule · upstream AWS Glue job completion · REST API call · Control-M workflow completion · job exit code |
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cross_tool.deps |
AWS Glue ETL jobs · AWS Glue Crawlers · Amazon S3 · Amazon Athena · Amazon Redshift · AWS Lambda · Amazon EMR · Apache Spark · REST API workflows |
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cloud.platforms |
AWS Glue ETL jobs · AWS Glue Crawlers · Amazon S3 · Amazon Athena · Amazon Redshift · AWS Lambda · Amazon EMR · Apache Spark · REST API workflows |
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error_handling |
configurable retry count · retry interval · exit-code evaluation · downstream cascade prevention · automated recovery workflows · SLA pre-breach alert · PagerDuty integration · Communication Suite alerts (Teams, Slack, Telegram, WhatsApp) |
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throughput |
batch processing up to 50 simultaneous jobs per Agent · scheduled data preparation · large-scale dataset transformation · parallel recipe execution · event-driven orchestration · enterprise-scale workflow automation |
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observability |
job-level audit log · end-to-end workflow monitoring · SLA tracking with breach prediction · dependency lineage visualization · Datadog integration |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across AWS Glue DataBrew, Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, AWS Lambda, Apache Spark, and cloud services in a single job flow—with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: Amazon S3 → AWS Glue Crawler → AWS Glue DataBrew → AWS Glue ETL → Amazon Athena → Amazon Redshift
- Data-aware triggers: file arrival, Glue job completion, DataBrew job completion, API event
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AWS Glue DataBrew |
Recipe execution · job scheduling · dependency orchestration · status monitoring · automated recovery |
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Amazon S3 |
File arrival detection · dataset validation · event-driven triggers · secure data handoff |
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AWS Glue |
ETL job orchestration · crawler completion detection · workflow sequencing · dependency management |
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Amazon Athena |
Query execution trigger · downstream dependency control · scheduled analytics workflows · completion monitoring |
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Amazon Redshift |
Data warehouse load orchestration · post-load validation · analytics workflow coordination · SLA tracking |
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AWS Lambda |
Serverless function execution · event-driven automation · API integration · conditional workflow logic |
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Apache Spark / Amazon EMR |
Spark job orchestration · cluster workflow coordination · dependency tracking · automated restart and recovery |
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 AWS Glue DataBrew workflows from one operational view.
AWS Glue DataBrew provides visibility into individual recipe jobs, but not the complete production workflow spanning upstream dependencies and downstream consumers. Control-M delivers centralized monitoring across the entire orchestration lifecycle, enabling operations teams to quickly identify issues and maintain reliable data pipelines:
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End-to-end workflow status
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DataBrew job execution history
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Upstream and downstream dependencies
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SLA breach prediction
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Centralized alerts and notifications
Control-M Monitoring dashboard displaying AWS Glue DataBrew jobs alongside upstream S3 events, AWS Glue workflows, and downstream analytics jobs with dependency relationships and SLA status.
SLA ASSURANCE
Keep AWS Glue DataBrew pipelines running on schedule.
Delayed data preparation can disrupt downstream analytics, reporting, and machine learning workflows. Control-M continuously monitors execution progress, predicts SLA risks before deadlines are missed, and automates recovery actions to keep critical data pipelines moving without manual intervention:
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Predictive SLA monitoring
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Automated retry policies
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Dependency-aware recovery
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Conditional workflow execution
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Real-time operational alerts
SLA dashboard highlighting a DataBrew workflow timeline, predicted SLA breach indicators, automated recovery actions, and downstream dependency status.
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
