Control-M for Enterprise Workload Automation
Control-M by BMC Software helps teams coordinate and automate data and application workflows across environments, including mainframes, the cloud, and hybrid systems. It gives users a single place to schedule jobs, track progress, and handle dependencies.
The platform also connects with popular cloud services, data tools, and DevOps systems, making it easier to manage production processes from start to finish.
Discover Control-M architecture, its features, benefits, and shortcomings within the enterprise ecosystem.
Control-M overview
Control-M by BMC Software helps organizations automate and coordinate workloads across both mainframe and cloud environments. It provides a single interface for monitoring and managing production processes, making it easier to track progress and manage dependencies.
The platform provides visibility into business operations, supports compliance requirements, and simplifies the management of complex workflows. It includes features such as data pipeline orchestration, secure file transfers, and integration with a wide range of enterprise tools.
Integrations
Control-M workload automation integrates with major cloud services, including AWS, Azure, and Google Cloud, for workflow execution across cloud providers. Data platform integrations include Airflow, Snowflake, and Azure Data Factory for compatibility with existing data stack architectures. DevOps tool support covers Jenkins, GIT, and CI/CD platforms for development process integration.
Security integration includes CyberArk for access management and compliance requirements. The platform’s code-based configuration supports version control and testing within software development lifecycles. Integration capabilities extend automation across application and data environments while working with existing technology investments.
The system maintains compatibility with both legacy mainframe systems and modern cloud-native applications through standardized integration approaches.
Control-M architecture
Control-M uses a distributed structure made up of three main components that work together to manage workflows across different environments. Control is centralized through Control-M/Enterprise Manager, while execution is distributed across multiple servers and agents.
Core components
Control-M/Enterprise Manager (Control-M/EM)
This is the central console for all Control-M/Servers. It allows users to view, monitor, and manage batch workflows across the organization. Control-M/EM includes client tools, server processes, and infrastructure services that handle communication and data flow between components.
Control-M/Server
This component acts as the scheduling engine. It manages job scheduling, workflow coordination, and processing activities. Each server runs on its own platform and maintains a local database containing information about active jobs.
It also balances workloads and handles requests from Control-M/EM. The system supports both distributed Control-M/Server setups and Control-M for z/OS on mainframes.
Control-M/Agent and Remote Hosts
These components carry out jobs based on instructions from the assigned Control-M/Server. Organizations can install agents directly on each machine or use Remote Hosts for agentless execution. Agents also support additional functions such as counters, multiple notification types, and plug-ins for specific applications.
Control-M/EM sub-components
Control-M/EM clients include multiple interfaces for different user roles:
- Control-M for production definition and monitoring
- Configuration Manager for system management and security
- Self-service for web-based service analysis
- Workload Change Manager for workflow modification requests
- Automation API for developers and DevOps integration
Control-M/EM servers handle communication and specialized functions:
- GUI Server manages client-server communication with load-balancing capabilities
- Global Conditions Server distributes events for cross-server dependencies
- Gateway components facilitate Control-M/EM to Control-M/Server communication
- Web Server provides HTTP/S access for various applications
- SLA Manager, Forecast Server, and Self Service Server support add-on functionality
Services architecture
Control-M uses a microservices architecture, consisting of independent services that handle specific tasks. This setup helps reduce system load and improve efficiency across the environment.
Control-M/EM Services
These services include tools such as Apache Kafka for data streaming and Apache Zookeeper for distributed coordination.
Other components include the Services Health Monitor for checking system status and several specialized services for validation, reporting, and workflow analysis. The Services Configuration Agent monitors all service operations to ensure they run correctly.
Control-M/Server Services
Server services are responsible for functions such as request routing, job management, and scheduling. They include an API Gateway to route requests, a Job Info Service to manage logs and job data, a Job Order Service to handle job requests, and a Scheduling Service that manages timing and dependencies. These services operate independently but remain synchronized via Kafka messaging.
Figure 1: Control-M architecture design.1
User evaluations
Note: The sorting is based on the number of reviews from B2B review platforms such as G2,2 Capterra,3 and TrustRadius.4
Advantages
- Control-M provides secure, centralized job scheduling and monitoring, with the ability to define job dependencies and rerun failed jobs.
- User interface includes color-coded job status and an alert window for quick action on failed or late jobs.
- The software supports the automation of various job types, including batch and API jobs, with easy setup and scheduling features.
Shortcomings
- Users report that Control-M’s integration and upgrading processes need improvement due to complexity and time consumption.
- Job failure reasons need to be clearer, and bugs following updates or patches have negatively affected daily activities.
- Users find the licensing costs high for small and mid-sized businesses, and the process for moving workflows to higher environments difficult.
Control-M for Big Data
Control-M helps organizations manage and automate data workflows in large-scale processing environments. It simplifies how teams build and run data pipelines, reducing the effort needed to move data from collection to analysis.
The platform connects with standard big data tools to coordinate complex processing tasks and keep workflows running reliably. It gives users a clear view of each stage in the data pipeline, helping them track progress and quickly resolve issues that might affect critical data operations.
By handling scheduling, dependencies, and automation, Control-M enables consistent, efficient data processing across an organization’s big data ecosystem.
Control-M managed file transfer
Control-M managed file transfer provides secure file movement capabilities integrated with workflow orchestration. The system supports multiple protocols, including SFTP, FTP over SSL, AS2, and PGP encryption for secure data transmission. Cloud storage integration includes Amazon S3, Azure Blob Storage, Azure Data Lake Storage Gen2, Google Cloud Storage, Oracle Cloud Storage, and Microsoft SharePoint.
The solution provides FIPS compliance and policy-driven processing rules to meet regulatory requirements. File transfer operations integrate with application workflows through a unified interface, providing consolidated visibility across both file movement and related workloads.
The system includes self-service capabilities enabling internal teams and external partners to manage file transfers independently. Advanced analytics and customizable dashboards enable monitoring of file transfer operations across cloud and on-premises infrastructure.
Figure 2: The graph showing the Control-M managed file transfer processes.5
Jobs-as-code
Jobs-as-code integrates workflow definitions into software development lifecycle processes using familiar development tools. The approach enables workflow automation via CI/CD pipelines, leveraging JSON, Python, Jenkins, and Git for version control and testing. Developers can code jobs using standard text editors or IDEs within automated CI/CD frameworks.
The methodology supports existing build tools for automation development and automated testing using established testing frameworks. Deployment capabilities extend to downstream environments through standard software deployment practices.
Community solutions include operational tasks such as agent status monitoring, agentless scheduling, workload policy management, and user role modifications. CI/CD integration examples cover GitLab pipelines, folder cleanup utilities, and Control-M artifact management within development workflows.
Infrastructure-as-code capabilities include Control-M deployment in Kubernetes pods, AWS Lambda integration, and Terraform provisioning. IDE integration provides Control-M function access and code snippets within development environments. API gateway connections enable Control-M REST services to be accessed through enterprise API management platforms.
Check out the video below to see how the job-as-code approach works in real life.
Control-M for SAP
Control-M for SAP manages workflow orchestration across SAP environments, including SAP BTP, SAP ECC, SAP S/4HANA, SAP BW, and data archiving systems. The solution provides native integration with SAP systems while supporting the RISE with SAP model. Job definitions can be imported into existing SAP workflows using Control-M conversion tools.
The platform manages processes including order-to-cash, procure-to-pay, payroll, year-end and month-end closing, and archiving operations. SAP event triggers can be activated with continuous monitoring and user-defined follow-up actions. The unified view eliminates the need for custom scripts while providing visibility across SAP and non-SAP systems.
Customer implementations include global SAP job scheduling across multiple production plants with tens of thousands of SAP jobs and managed file transfer operations. Teams, including support, job creation, and SAP specialists, can collaborate through the platform’s unified interface. Integration capabilities extend to products like Informatica alongside SAP systems.
Control-M for mainframe
Control-M for mainframe provides workflow orchestration for mainframe environments while enabling integration with multi-cloud systems. The solution manages the delivery of mainframe business services through native application workflow orchestration. Integration capabilities reduce the manual processes required to orchestrate mainframe-to-cloud workflows and data pipelines.
The platform supports migrating mainframe applications to cloud environments while optimizing workflow execution to reduce processing costs and meet service-level agreements.
Report management includes collection, retrieval, distribution, and archiving capabilities for reducing storage and distribution costs. JCL management ensures error-free job control language in application workflows while eliminating manual restart procedures.
Businesses can leverage Control-M to consolidate scheduling tools across mainframe, distributed systems, and cloud environments into a unified interface. Production job management includes mainframe, Informatica, and other enterprise software jobs via the Control-M platform.
What’s new in Control-M
The latest Control-M release focuses on workflow orchestration enhancements across hybrid and multi-cloud environments with improved deployment flexibility and SaaS migration capabilities.
Control-M Data Assurance overview
Control-M Data Assurance adds built-in data validation to workflow automation, helping teams confirm data accuracy as processes run.
Key features are:
- Automated: Validation checks run as part of the workflow, giving users a single place to view data quality results.
- Comprehensive: Data moves reliably across different systems and applications, with continuous visibility into each stage.
- Accessible: Data assurance integrates into existing workflows, so teams don’t need additional coding or data science expertise.
Key benefits include:
- Reduce risk and cost: Identify and stop incorrect data early to prevent processing errors, missed deadlines, and unnecessary expenses.
- Save time and streamline work: Add validation directly into Control-M workflows without custom scripts or complex setup.
Hybrid and multi-cloud orchestration
Control-M provides enhanced deployment capabilities for cloud and hybrid environments, with improved scalability. The platform supports adaptable deployment models that accommodate varying infrastructure requirements across organizations. SaaS transition capabilities offer a simplified migration path while maintaining existing functionality and value propositions.
Key integrations strengthen Control-M capabilities by connecting with specialized tools, including CyberArk for security management and application performance monitoring solutions such as Datadog, AppDynamics, and Dynatrace.
These integrations extend the platform’s monitoring and security capabilities within existing enterprise technology stacks.
Jett generative AI advisor
Jett functions as a GenAI-powered advisor specifically designed for Control-M SaaS environments. The system provides instant workflow expertise through natural language query capabilities, enabling users to ask workflow-related questions and receive answers in simple, understandable language.
The AI advisor accelerates key workflow scenarios, including:
- Issue resolution through rapid problem identification
- Audit compliance verification with simplified data access
- Workflow optimization through pattern analysis
- Anomaly detection for proactive management
Figure 3: Control-M generative AI assistant Jett’s dashboard.6
Further reading
- Top IT Process Automation Tools
- Top IT Automation Trends, Predictions & Stats
- Top IT Automation Use Cases
- Benchmark Best IT Automation Software
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.