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Expert articles, guides, and video tutorials on Control-M, workload automation, migrations, best practices, and enterprise optimization.
Control-M for AI Agent Orchestration
How Enterprise Workload Automation Becomes the Governance Layer for AI Operations. Covers native integrations for CrewAI, LangGraph, Snowflake Cortex, and the Control-M MCP Server.
Control-M SaaS Migration: Complete Step-by-Step Guide
Migrating to BMC Control-M SaaS requires careful planning and execution. Learn the comprehensive approach to assessment, planning, execution, and optimization for a successful migration.
Control-M vs Airflow: Which One Should You Choose?
Compare Control-M and Apache Airflow to understand which workload automation solution is best for your organization's needs.
Control-M Upgrade Guide: From v9.0.xx to v9.0.22
Learn the step-by-step process for upgrading Control-M within the 9.0.x version family with minimal risk and downtime.
Replacing Broadcom Scheduling Tools with Control-M
Consolidate legacy schedulers (CA7, ESP, AutoSys, Automic, dSeries) onto a unified Control-M platform for better governance and cloud integration.
Workload Automation Architecture for Enterprises
Understand the core components and best practices for designing a scalable, reliable workload automation architecture.
Control-M for SAP: Best Practices
Implement Control-M in SAP environments using proven best practices for naming, variants, dependencies, and batch optimization.
Control-M Optimization: Improve Performance and Reduce Costs
Discover techniques for optimizing Control-M environments to improve performance while reducing operational costs.
Control-M vs Redwood RunMyJobs
Compare Control-M and Redwood RunMyJobs to determine the best platform for your enterprise automation needs.
Control-M API Automation: Unlocking Advanced Use Cases
Leverage Control-M APIs to extend automation capabilities and integrate with CI/CD pipelines and enterprise systems.
The Business Value of Workload Automation: ROI and Cost Justification for Control-M
Workload automation delivers significant business value beyond basic job scheduling. Learn how to quantify the ROI of Control-M and build a compelling case for modernization.
Hybrid and Cloud Workload Automation: How Control-M Enables Modern IT Operations
As organizations move toward cloud and hybrid infrastructures, Control-M provides a unified solution for managing distributed workloads across on-premise, cloud, and multi-platform environments.
Enterprise Job Scheduling: Why It Still Matters
Understand why job scheduling remains a fundamental component of enterprise IT operations and modern digital initiatives.
White Papers
In-Depth Research & Guides
Free white papers covering Control-M best practices, AI agent orchestration, and enterprise automation strategies.
Control-M for PeopleSoft Orchestration
Beyond the Process Scheduler: How Control-M becomes the enterprise orchestration layer for every PeopleSoft workload. Covers Jolt protocol integration, cross-system dependency management, SLA alerting, Oracle Cloud co-existence, and Jobs-as-Code for PeopleSoft CI/CD.
Control-M for SAP Orchestration
Beyond Built-In Scheduling: How Control-M becomes the universal orchestrator for every SAP workload. Covers RISE with SAP, S/4HANA Cloud, SAP IBP, SAP BTP, cross-system dependency management, and Jobs-as-Code for SAP CI/CD pipelines.
Control-M for the Modern Data Stack: Snowflake, Databricks, dbt, and Beyond
Why Enterprise Workload Automation is the missing governance layer for cloud-native data pipelines. Covers Control-M integration with Snowflake, Databricks, dbt, Apache Airflow, and how to enforce SLAs, manage cross-system dependencies, and connect your data stack to the rest of the enterprise.
Control-M, GitHub Actions, and ChatGPT: A Modern CI/CD Solution for Workload Automation
How VPMA built a fully automated, AI-driven job management pipeline using Control-M Jobs-as-Code — integrating GitHub Actions and ChatGPT for conversational, governed workload automation delivery.
Control-M for AI Agent Orchestration
How Enterprise Workload Automation Becomes the Governance Layer for AI Operations. Most enterprises are running AI agents on cron jobs with no retry logic, no dependency management, and no audit trail. This white paper explains how BMC Control-M provides the production-grade scheduling, governance, and observability layer that AI agent deployments need — with native integrations for CrewAI, LangGraph, Snowflake Cortex, and the new Control-M MCP Server.
Video Tutorials
Control-M Video Series
Watch our expert-led video tutorials on Control-M implementation, optimization, and best practices.
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