Disconnected tools. Missed SLAs. Broken pipelines. For many data teams, the challenge isn’t building—it’s making everything work together reliably.
Control-M bridges that gap. It connects the technologies you already use—like Snowflake, SageMaker, and Tableau—into unified and resilient workflows.
Here’s how Control-M helps you orchestrate across your entire data stack, delivering operational efficiency without adding complexity.
Orchestrate full ML pipelines with Control-M and Amazon SageMaker
Machine learning delivers real value only when it reaches production. Control-M helps you get there faster by transforming SageMaker workflows into orchestrated, resilient pipelines.
Instead of hand-cranked scripts and brittle logic, Control-M offers:
- Direct integration to launch and monitor SageMaker training and inference jobs
- Built-in SLA tracking, parallel job execution (up to 50 per agent), and predictive alerts
- Seamless coordination with upstream data prep and downstream deployment steps
Real-world example: A data science team used Control-M to automate everything from data prep to monitoring. The result: a repeatable ML lifecycle that integrates directly with CI/CD pipelines.
Run smarter ELT workflows with Snowflake
Snowflake is built for scale—but it takes orchestration to turn SQL jobs, UDFs, and transformations into stable, production-ready workflows.
Control-M integrates directly with Snowflake so you can:
- Automate, orchestrate, and monitor SQL and UDF executions with clear visibility
- Automatically trigger downstream steps based on completion status
- Ensure consistency and data integrity across platforms and tools
Use case: Orchestrate an end-to-end pipeline from Kafka ingestion to Snowflake transformation to Tableau visualization—all within a single, governed Control-M workflow.
Scale Airflow with centralized orchestration
Apache Airflow is great for DAG orchestration—but complexity can grow quickly. That’s where Control-M comes in.
Control-M works with Airflow to:
- Centralize DAG execution and integrate with other toolchains
- Track job status visually with SLA and error-handling logic
- Trigger Airflow jobs from upstream events or use them within larger workflows
Best of both worlds: Use Airflow where it excels but orchestrate across your stack with Control-M to ensure continuity and control.
Deliver always-fresh analytics with Power BI, Looker, and Tableau
Dashboards only add value when they reflect current, accurate data. Control-M connects BI platforms to the rest of your pipeline, ensuring updates happen on time, every time.
Through native and API-based integrations, you can:
- Trigger dashboard refreshes after upstream ELT or ML jobs complete
- Link model outputs directly to reporting tools and decision-making
- Monitor and resolve issues proactively before end users are affected
Example: Push SageMaker inference results into Power BI or Tableau dashboards immediately upon model run completion—no manual intervention required.
Why Control-M?
If you’re running modern data platforms across multi-cloud environments, Control-M helps everything run better—without reinventing your stack. It delivers:
- Unified governance: Centralized visibility, management, monitoring, and alerting
- Resilience: Auto-recovery, retries, and SLA intelligence built-in
- Seamless integration: Up to three integrations added monthly, including AWS, Azure, GCP, and many more
Control-M is the orchestration engine that makes your stack smarter—by making it work together.
See it in action
Whether you’re deploying models, syncing dashboards, or scheduling ELT jobs, Control-M helps you build smarter workflows that don’t break under pressure.
Explore real-world demos and integration guides—no forms, no sales pitch.
Take the Control-M product tour to see how it fits your stack.
