Control-M SaaS Starter Pack

My primary use case for Control-M is heavy-duty security data pipeline orchestration and automated workflows running strictly on-premises.

A great example of a workflow I run recently on Control-M is my automated nightly security log ingestion and threat intelligence staging pipeline for an on-premises enterprise client.

My main use case for Control-M is on-premises security data pipeline orchestration.

Control-M supports several core services and business use cases across organizations from a broad enterprise perspective.

Regarding specific workflows, while dedicated data science platforms have native pipeline tools and orchestration, ML operations across a hybrid enterprise environment often requires integrating platforms like Amazon SageMaker or Google Cloud Vertex AI directly with the broader AI and security infrastructure.

My organization utilizes Control-M as a core operational backbone for orchestrating sensitive on-premises security data pipelines, automated managed file transfer routines, and local log staging tasks that feed directly into my SIEM ecosystems like Splunk and FortiSIEM.

I use Control-M to orchestrate workloads across multiple environments, mostly on-premises and virtual.

The biggest positive impact I have seen from Control-M is zero pipeline blind spots because everything runs on strict dependency mapping, and log ingestion and threat intelligence syncs never fail silently.

While I don't look at the business ROI dashboards all day, from a day-to-day SOC operation standpoint, I track a few key metrics and outcomes with Control-M.

The standout feature of Control-M for me is its end-to-end workflow dependency mapping combined with its managed file transfer capabilities.

Regarding AI capability with Control-M, while my primary focus with Control-M is handling secure, on-premises data pipelines, I have also looked at AI capabilities and security.

Control-M utilizes its built-in AI assistant like Jet and Natural Language Processing to help build and troubleshoot workflows.

In my SOC and MSSP environment, I rely on Control-M's built-in approval workflows and risk gating to ensure that high-impact automation or pipeline modifications do not go live unmonitored.

Control-M smooths and modernizes workflows by shifting from traditional GUI-driven configuration to an API-first workflow-as-code model in terms of ease of integration with technology of data operations and DevOps processes.

Control-M, through the lens of business user visibility and stakeholder transparency, offers a strong bridge between raw technical execution and high-level operational outcomes.

Control-M supercharges my data operations and DevOps initiatives by bridging traditional IT orchestration and application delivery.

When integrating AI and ML operations workflows into my security operations, Control-M acts as a critical runtime governance layer in terms of integration governance.

From a SOC perspective, there are a couple of areas where Control-M could be improved for security operations.

Sometimes, brief temporary network drops trigger immediate failure notifications before Control-M's internal retry logic has a chance to kick in and successfully complete the task.

I have been using Control-M for around three to four months.

For workflow automation, data pipelines, and MFT operations, stability is a core metric that separates Control-M from lightweight schedulers and native cloud cron tools.

Control-M achieves enterprise-grade scalability through its decentralized three-tier architecture, allowing my MSSP to seamlessly scale from tens of nodes to thousands of simultaneous endpoints.

For a mission-critical orchestration engine, Control-M customer support is vital for my MSSP SOC.

When deploying Control-M in an MSSP or SOC environment like mine, I follow a structured phase approach focused on local control and security.

For my specific use case, I deployed Control-M strictly on-premises.

Deploying and managing an on-premises Control-M environment within a hybrid SOC or MSSP structure requires a team highly specialized in it because Control-M acts as the foundational mission-critical platform.

The automated threat intelligence and log staging pipeline transformed my operations. Before Control-M, my MSSP SOC relied on a custom chain of scripts.

When evaluating Control-M from an enterprise and MSSP budget perspective, it sits firmly within the category of premium mission-critical infrastructure software.

I gave Control-M an 8 out of 10 because it is an absolute powerhouse for on-premises security data pipeline orchestration, managed file transfer, and strict dependency mapping.

Control-M is used by a few specific roles depending on whether they are building, monitoring, or auditing the work.

When I integrate AI workloads and drive adoptions, especially around automated security data pipelines and orchestrations, I focus heavily on balancing agility with strict security controls.

Assessing the creation and automation of data pipelines across on-premises environments requires balancing infrastructure control, deterministic reliability, and strict security compliance.

My advice for engineering teams and organizations evaluating Control-M, especially those operating in high-stakes environments such as security operations, MSSPs, or complex data engineering pipelines, is to adopt a jobs-as-code approach from day one.

My use case is workload automation. Control-M adapts to my environment by using data pipelines that are orchestrated through Control-M, providing all the data needed for machine learning initiatives.

Regarding specific machine learning operations workflows, I have orchestrated them by integrating Control-M with tools such as MLflow. I have primarily focused on integrations and DataOps. Beyond MLflow, I have also integrated Snowflake and Vortex. Snowflake executes SQL commands and handles data format and data transformations. Once data transformations are completed using Snowflake, Kafka, and Vortex, multiple integrations are performed through API with agents, and Vortex is also utilized.

For ITSM and change management tools, I have integrated Control-M with these systems.

For change management workflows, I create scenarios such as user creation in Active Directory, where the workflow executes and calls API scripts or automation for Active Directory to create the users. These workflows have been created for release management. Once a change is approved internally within the ITSM system with all necessary approvals from the CAB, it is critical to ensure the release happens according to the change guidelines. This requires a sophisticated and reliable system with robustness. Control-M allows me to perform the change and ensure that the data is returned to the ITSM.

The benefits I have seen from implementing Control-M include increased productivity by ensuring jobs are completed based on prerequisites. Control-M reduces my complete total cost of ownership through increased visibility, which reduces multiple tool sprawl and results in cost reduction. I have achieved increased productivity, better SLA management, and ensured that SLA records, SLA points, and SLA metrics meet KPIs. Control-M provides automated failure recovery so that remediation is also taken care of.

Moving into DataOps operations, Control-M ensures that my AI infrastructure and AI initiatives are all supported. The advantages include reduction in cost, increase in productivity, and ensuring compliance.

The best features I find in Control-M are its robustness and job visibility of complex job dependencies and status. When I have a mesh of jobs, Control-M can clearly identify and update and also run all these multiple jobs while providing clear output. Control-M offers robustness, visibility, and scalability.

Control-M helps with policies, approval, and risk gating capabilities. It is very robust in handling workflow approvals within my organization with multiple levels of approvals, even with stable rollbacks and clear status updates. Notifications are proper and informative. Control-M is the right fit for my needs.

Regarding visibility, Control-M provides full clear visibility of all processes. It has centralized visibility so that multiple platforms, multiple orchestrators, and different functionalities across departments can all be visible from a single pane of glass.

Control-M sits on top of many orchestration tools that are available in the market. One area for improvement is that I still have dependency on orchestration tools. If Control-M develops or acquires an orchestration tool and builds that capability within Control-M as a complete orchestrator, similar to Ansible or Terraform, my dependency on external orchestrators would reduce and I would use Control-M more extensively as a complete end-to-end tool. Building a full-fledged orchestrator inside Control-M would be ideal and would be a wonderful value addition.

Aside from an orchestrator, there might be value in performing some functions without headless architecture. An orchestrator capability should provide that ability. Control-M is bringing AI and LLM integrations, which is positive. The orchestrator capability is the main area for improvement at this time.

I have been using Control-M for more than five years. Throughout my five years of association with Control-M, I have found it to be successful when dealing with complex workload automation tasks.

Control-M is very stable. I rate its stability as a nine out of ten.

Control-M handles very large numbers of jobs, going beyond 10,000 jobs. My organization has many BMC Control-M specialists, and I have implemented Control-M organization-wide. My clients are large organizations, very large. My organization has 150,000 people. Control-M is very much scalable. I rate its scalability as a nine out of ten.

I rate the technical support at nine out of ten.

The return on investment I see is at least something like 30% easily.

Regarding pricing, Control-M is expensive compared to the competition, but it is worth it. Even though Control-M is expensive, pricing could be brought down slightly. One more improvement I was looking at from a pricing perspective is that SaaS pricing is too expensive. On-premises pricing has no limitation on the jobs and licenses, but when moving to SaaS, there is a big impact.

Control-M is fantastic because of its support, which is number one. The ability to scale up and the best implemented solution across the globe make Control-M stand out. There are competitors such as ActiveBatch and other companies, but Control-M is still the number one in workload automation.

My business role is as a partner and system integrator. I have Control-M deployed in every mode, both cloud and on-premises as well as in private cloud. For cloud services, I use prominently Azure and AWS.

My relationship with BMC is strategic and transformational. I would describe Control-M as positioned for performance in building, scheduling, and managing workflows. My final rating for the solution overall is around nine out of ten.

My use case is workflow automation, and I work as an administrator for Control-M. I previously worked on mainframe environments, but now I work on distributed systems.

Control-M does not currently support AI or ML workloads at version 9.0.21, but AI support will be available in version 27, which I plan to install next year.

Regarding policies, approval, and risk-gating capabilities for automated workflows, I use an in-house product to automate the workflow between several environments, so I do not use a solution from BMC for this purpose.

I do not use Control-M with SAP solutions such as SAP S/4HANA or BW.

One of the challenges I was looking to overcome with Control-M involved using PeopleSoft. I use BMC with an interface that was written in-house to start jobs on PeopleSoft.

Control-M integrates with my ITSM and change management tools through REST APIs that call a connection with HEAT to create tickets automatically. This integration is an in-house solution using API programs.

I do not use Control-M to support DevOps or DataOps initiatives.

Control-M saves approximately 20% of my time, though it is difficult to quantify precisely.

The best features of Control-M are the graphical interface, which I appreciate the most about the product.

Control-M is one of the most popular and most complete products available for workload automation.

Regarding integration capabilities with external products and my security or technical stack, the ease of integration is reasonably good. I have integrated Azure jobs, DB2, and SQL, and I am looking for Micro Focus integration, which is not provided by either BMC or Micro Focus. These are the integrations I currently use, but I expect additional integrations in the future.

Control-M performs exceptionally well in building, scheduling, managing, and monitoring workflows. There are extensive possibilities and numerous REST APIs available to manage the workflow, providing a very broad spectrum for monitoring everything in the workload.

Support from BMC has room for improvement, especially regarding the Micro Focus environment. I have encountered several issues in the past where I needed to contact the lab environment to obtain correct solutions for installations, particularly with the Windows fault-tolerant mechanism.

I have been working with Control-M for more than 10 years.

Overall, Control-M stability is good, though I sometimes experience problems related to Windows issues with memory or similar issues that can cause problems with Control-M.

I rate the overall stability as a nine out of ten.

Approximately 500 users use Control-M in my organization.

I rate the scalability as a nine out of ten.

I rate support from BMC as an eight out of ten.

Prior to using Control-M, I used Tivoli OPC on mainframe. When my organization merged with another firm, I had to migrate to Control-M on mainframe.

I think Control-M is expensive, but I do not have complete information about the full pricing details.

I do not have recent experience with other schedulers on distributed systems, but on mainframe, I had experience with CS7.

Control-M is currently deployed on-premises, and I do not have plans to move to the cloud in the near future.

Control-M does require maintenance. The maintenance is not always easy or complex, but as I mentioned before, I work in a Windows environment, and that environment is not always correctly documented by BMC. For other environments such as Linux and Unix, there seems to be no problem, but I have encountered issues from time to time on Windows.

I recommend Control-M as a good solution, with the exception that it is not the best solution to deploy on Windows. Organizations should conduct a proof of concept before implementation.

My main use case for Control-M is to automate and schedule workflows and manage batch jobs, monitor dependencies, and ensure critical business processes run smoothly without any manual intervention.

I mainly use Control-M to automate daily data processing, financial reporting, database updates, and schedule application jobs because it helps ensure data is available on time and reduces manual work across business operations.

We are using Control-M for financial services.

We use Control-M for approval mainly for sensitive financial data processing, production deployment, and critical business workflows. It helps ensure proper authorization, follow company policies, and reduce risk before important jobs run automatically. This also improves accountability and compliance.

We mainly use Control-M to schedule and control workflows, including data preparation, model training, and deployment tasks. Integration with tools such as Azure ML and MLflow can help manage dependencies, automate execution, and improve overall workflow reliability.

Control-M supports our DataOps and DevOps initiative by automating data pipelines, managing dependencies, and scheduling application deployment. It improves coordination between teams, reduces manual tasks, and helps ensure reliable and timely delivery of our business processes.

I use Control-M to orchestrate workloads across on-premises environments. It helps manage job dependencies, centralize monitoring, and maintain consistent execution across different platforms. This improves flexibility and makes operations easier to manage.

We use Control-M to manage both SAP and non-SAP workflows. It helps schedule and monitor SAP ECC and BW jobs alongside database operations, data transfer, and application tasks. Centralized monitoring makes it easier to manage dependencies and ensure smooth execution across different systems.

We use Control-M to manage data processing tasks through job dependencies, scheduling, and automated workflows. It helps coordinate tasks across databases, SAP systems, and cloud platforms. We monitor job statuses through a centralized dashboard and use alerts to quickly handle failures. This ensures smooth data flow and reduces manual effort.

Our DataOps and DevOps stack includes Control-M, SQL databases, SAP systems, and a cloud platform. We use these technologies for data integration, workflow automation, application deployment, and job monitoring. Control-M helps connect different tools and manage dependencies, ensuring smooth execution across environments.

We are planning to use the Control-M Python client or API in the coming days.

Control-M has reduced troubleshooting time by providing real-time job monitoring and instant failure alerts. My IT team can quickly identify failed jobs, check error details, and resolve issues before they impact daily business operations.

Control-M has improved our overall operational efficiency by reducing manual work, minimizing job failures, and ensuring timely data processing.

Control-M has helped us save around 25 to 30 percent of manual monitoring time. We have also seen fewer job failures, faster issue resolution, and better SLA performance through automated scheduling alerts.

Control-M helps free up IT personnel by automating repetitive scheduling, monitoring, and troubleshooting tasks. This allows my team to focus more on important activities such as system improvement, security, and business-critical operations. This is very important for our organization because it improves productivity and reduces operational workload.

Control-M has helped us achieve faster workflow delivery, improved SLA performance, and fewer job failures. Automation has reduced manual effort and improved error handling. Better monitoring and audit trails have also strengthened operational efficiency and compliance across our organization.

Control-M has improved our daily operations by automating job scheduling and reducing manual monitoring. We have reduced manual effort by around 30 percent and improved job completion time by 20 percent and reduced delay through faster failure alerts. It also improved SLA management and overall team productivity.

Control-M is helpful because it simplifies workflow management, reduces operational complexity, and brings all workflows under one platform.

Before implementing Control-M, audit preparation involved manually collecting job execution reports and approval records from different systems that took too much time. After implementation, everything is centralized. Automated logs and audit trails have made it easier to retrieve information, verify activity, and prepare compliance reports very quickly.

Control-M offers many features such as easy job scheduling, workflow automation, real-time monitoring, dependency management, and failure alerts. I especially like its centralized dashboard because it helps me track jobs, identify issues quickly, and reduce manual efforts.

Control-M's AI capability provides fairly accurate and reliable output for monitoring and identifying workflow issues. These capabilities help analyze job patterns and highlight potential problems. However, output should still be reviewed by a team, especially for security and governance-related decisions.

Control-M integrates well with AI and ML tools by supporting automated workflows, data pipelines, and scheduled model execution. It helps manage data processing, monitor ML jobs, and ensure timely execution. Better integration with AI platform predictive monitoring would make it even more useful for us.

The approval process in Control-M is fairly easy once everything is configured. It helps organizations maintain better control over critical jobs, avoid unauthorized changes, reduce operational risk, and improve compliance. It also gives the team more confidence in automated processes.

Control-M provides good visibility through its dashboard and allows business users to track job statuses, monitor progress, and identify failures without depending completely on our IT teams. This is important for our organization because it improves transparency, saves time, and helps the team make decisions more quickly.

Control-M requires clear audit trails, proper tracking of data and model changes, and role-based access for production AI workloads. Control-M helps monitor workflow execution, maintain accountability, and enforce approval processes. These controls are important for security, transparency, and compliance.

Control-M helps us manage workflows across on-premises infrastructure. We use it to schedule jobs, manage dependencies, transfer data, and monitor execution across different environments. This improves visibility, reduces manual efforts, and ensures smooth coordination between all environments.

Control-M performs very well in building, scheduling, managing, and monitoring workflows. Its interface makes it easier to create jobs, manage dependencies, automate repeated tasks, and track execution in real-time. Failure alerts and centralized monitoring help my team resolve issues quickly. I believe it improves productivity, reduces manual effort, and ensures reliable workflow execution.

Control-M helps us meet SOX and audit requirements through detailed job execution logs, audit trails, role-based access, and approval workflows. These features help track who executed or modified jobs, maintaining accountability and providing useful records for compliance checks and regulatory reporting.

Control-M integrates with ITSM tools such as ServiceNow to improve incident management and approval processes. It helps automatically raise alerts for failed jobs, create incidents, and track resolution statuses. This improves communication between teams, speeds up troubleshooting, and ensures better control over production changes.

Workflow orchestration is very important for our DataOps and DevOps initiative. Control-M helps us automate production workflows, manage dependencies, and monitor jobs across different environments. It reduces manual efforts, improves delivery speed, minimizes errors, and ensures critical business processes run reliably.

Centralized management of workflows across on-premises servers is very important for our organization. It improves visibility, simplifies scheduling, reduces manual efforts, and ensures reliable execution across different environments. It also gives us flexibility to scale as business needs grow.

Control-M has made it simpler to create, integrate, and automate data pipelines across on-premises. Its centralized interface helps manage job dependency, schedule workflows, and monitor execution. Although initial configuration takes some time and effort, it reduces manual work and makes overall pipeline management easier.

Control-M can be improved by making the interface more user-friendly, simplifying workflow configuration, and providing better error messages because sometimes we face issues. Faster troubleshooting, smarter failure prediction, easier cloud integration, and more detailed reporting would also make our daily job management even smoother.

Control-M can improve AI capabilities for governance and security by adding intelligent threat detection, automated compliance checks, and unusual activity monitoring and role-based access recommendation. AI-based alerts and audit reports would help identify risk early and maintain better control over sensitive workflows.

One challenge is managing dependencies between Kubernetes jobs and cloud-native applications and traditional batch processes. Monitoring and troubleshooting across different environments can also be complex. However, Control-M provides centralized scheduling, better visibility, and automated workflow that is helping my team improve coordination, reduce manual work, and manage hybrid environments more efficiently.

I have been using Control-M for the past two years.

Control-M has been stable.

In our organization, approximately 50 to 60 percent of users interact with Control-M. Main role cases are Technical Architect that maintains all the infrastructure, as well as the IT team that continuously checks the transactions and how they are controlling our system without any impact on business operations.

Control-M is currently used across several important areas of our organization, mainly for job scheduling, data processing, workflow automation, and monitoring. We plan to increase its usage for cloud-based automated workflows, AI and ML operations, and DevOps processes to improve automation and efficiency.

Around 25 to 30 users across our organization are using Control-M, including IT operation engineers, DevOps engineers, data engineers, database administrators, and system administrators.

I would rate Control-M eight out of ten for scalability. The solution is very good.

My relationship with BMC is more transformative than transactional. BMC provides ongoing support and solutions that help us improve automation, modernize workflow, and manage complex operations. We see good value in long-term innovation, product improvement, and the ability to support our changing business needs.

BMC is very supportive.

I would rate customer service an eight.

We transitioned to Control-M from a traditional job scheduling approach to get better automation and centralized workflow management. The transition improved visibility, simplified job scheduling, and reduced manual intervention. It also made managing complex workflows across different environments easier.

I did not consider alternate solutions before Control-M.

The initial deployment takes around three to four months, including installation, configuration, workflow migration, integration testing, and user training.

We required a team that handles all IT operations, DevOps engineers, and data engineers. They handle all the operations so that we can deploy these things on our on-premises as soon as possible.

BMC helped us understand the migration process by providing guidance on planning, workload assessment, and migration strategy. Their support helped identify dependencies, prioritize critical jobs, reduce migration risk, and ensure a smoother transition to Control-M with minimum disruption to daily operations.

We have seen a good return on investment of approximately 50 to 60 percent reduction in overall cost.

We have also seen a good ROI of around 25 percent because it improved job completion efficiency and reduced operational delays. Faster issue resolution and fewer job failures have also helped us save time and improve overall team productivity.

Pricing is reasonable, considering the automation and operational benefits that Control-M provides. Initial setup and licensing costs can be slightly high, especially for smaller teams. However, the centralized management, reduced manual efforts, and improved productivity make it a worthwhile investment in the long run.

I would like to rate Control-M an eight because it offers reliable automation, easy job monitoring, and good workflow management. It saves my time and reduces manual work efforts. However, there is still room for improvement in user interface, configuration simplicity, and troubleshooting.

Control-M is very helpful for handling all the workload management because we can handle everything from a centralized dashboard. It reduces manual intervention.

I would recommend Control-M for anyone considering this solution for their operations.

My use cases for Control-M primarily involve scheduling, especially with applications like SAP, database plugins, and mostly OS.

Using Control-M has made it simpler to create, integrate, and automate data pipelines. We used Airflow to run data pipelines at one point, but it lacked many features that Control-M offered. Fortunately, Control-M had a plugin for Airflow, so we could use Airflow for data pipelines but schedule them via Control-M jobs, which gives us a native, feature-rich scheduling environment and integrates with data pipelines along with Airflow.

BMC keeps up well with the technologies and developments out there.

What I appreciate most about Control-M is its stability. It is stable and usually does not go down. The consistency when the application restarts is flawless, and there is no usual data loss with Oracle as the backend. It is dependable based on my experience as an administrator using BMC across organizations.

Control-M is easy to integrate with other applications and technologies because it provides plugins for the latest solutions by default. If not, there is an Application Integrator built for that purpose, and there are community-driven integrations. From an alerting perspective, BMC had a very good affinity to its own tools, and it did not have ServiceNow integration, which is the most popular ITSM tool. We ended up using ancient SNMP calls to get the alerts out to custom tools. Other than that, integrations are good.

What I dislike about Control-M is that until very recently, and even now, it was not containerized or stateless. It is a stateful application, which is what I still dislike about it. It prefers its host, tries to maintain the connection, and lives on the host. It is not fully containerized or split into microservices, and it cannot be installed on anything with a database connection to start working. It prefers the host on which it was originally installed, and the statefulness of the application is what I do not like.

I have been using Control-M overall for 19 years.

Control-M is stable. Only heavy upgrades require downtime of approximately 30 minutes for the Enterprise Manager and Control-M server applications every year or every six months. Other than that, it runs continuously.

Control-M likes its nodes and is stateful. You cannot just add multiple nodes. It operates on active-passive high availability and does not scale horizontally much. Though capacity-wise it is good, at a given point in time, an instance runs on one VM, not ten. If that goes down, it can switch over to another, but that statefulness is what I do not like about scalability.

It works, but it does not feel scalable. One node at a time is not scalable.

Control-M's technical support is good. For priority cases, they are quick. P1 and P2 cases respond at the earliest, while P3 takes some time based on the time zone you are in and the time zone you are signed up for. They have always been helpful and go out of their way to get things done for me.

I would give Control-M's technical support a score of nine out of ten.

I migrated from Tandem to Control-M as my organization used Tandem earlier. They brought in Control-M because they had mainframe and CS-7, and I believe that is how it started for the current organization. In the previous one, some brought it for SAP, some brought it because of the mainframes. We had cron before Control-M or the mainframe had JCLs being scheduled by the mainframe itself, and then we brought in Control-M in the 1990s.

The initial deployment of Control-M, from what I did hands-on with migrations from the OS and new projects, was seamless. I did not bring Control-M into any organization as such. I went when it was already there. I enhanced, refactored, and migrated everything. In a development environment, it is seamless. You might still sometimes need to call support, but the documentation and a case here and there, along with a little bit of experience using it, works adequately.

Open source tools like Rundeck are more straightforward and easy to install, set up, and manage for someone starting, but Control-M still needs expertise in this area to get it started and working since it has its own configs and the way it works.

Control-M has gotten better at its pricing and with the latest offer for minimal jobs, it is acceptable given the value it provides. Mainframe is a bit expensive regarding the mainframe components, but I believe that is the way for any of the mainframe components. For distributed scheduling, non-mainframe, Unix, and Windows, it is reasonably priced.

I have used alternatives like Rundeck, and people use cron. I have also used Airflow, which provided some other functionality. Rundeck and Airflow are alternatives, and I regard Rundeck as an alternative.

I prefer Control-M more than the alternatives. It is a paid tool, but it is worth it.

I started working in my current field overall with Control-M after my first year in IT, so that would be 18 years.

Control-M requires maintenance on my end for patches when there are vulnerabilities, which are usually released by Control-M. Other than that, everything can be automated.

Control-M takes care of all scheduling jobs, and when you have Control-M, you do not need to use any other tools. If one application has a single server running a couple of jobs, I would still suggest using cron as that would be more suitable. If complexity arises, you can better use Control-M. Because it is not free and not unlimited, you pay per job license. Whenever something simple can be triggered by cron and easily managed, I would personally use cron on a single server with a handful of jobs. If I need alerting, monitoring, a GUI, integrations, and audit and compliance, I would switch to Control-M even if I have to pay per job. Competing scheduling tools are not usually required as Control-M will suffice with data transfer, mainframes, distributed, and everything.

My relationship with BMC is balanced between transactional and transformative. It has been good, but it depends on the organization's priorities. There was a push towards going to open source schedulers, wherein we as an organization had to wind down the job licenses. The licensing calls are always tricky and handled by the vendor management team. They have their own logistics. I am a purely technical person making technical decisions, and I probably push something if it is good, but then it would go that way. BMC genuinely tries to help their customers. They have built us plugins, but at the same time, it is about the money.

Overall, I would give Control-M a rating of eight out of ten because it is not vertically scalable, and they did not fully containerize it or make it into microservices. For that reason, I would give it an eight. If not for those issues, I would give it a nine.

Control-M is used to run processing tasks needed for applications. At Europcar, I mainly used it for various applications where processing steps had to be executed in a certain order. For CNAM, it was the same: processes that had to run in a certain order, but with Kubernetes in parallel. There, it was mainly to generate very important files and user files that were critical and had to be generated regularly, then sent via SecureTransport. For Rexel, it is the same thing: processing for various applications that must run either to generate reports or to execute various commands on the servers.

A specific example of a process I orchestrate with Control-M is for CNAM, where it was about generating reports that provided complete tracking for each user, that is, each patient who had an illness, and doing that daily. Once the processing had been done on Kubernetes, a file would be generated, and I then checked with Control-M that this file was indeed present, using File Watchers, and then sent it via command line using SecureTransport.

Currently, I could say that at Rexel we use Control-M in a DevOps way, meaning that to move from one environment to another we use Azure DevOps through the Control-M API. I export the JSON definitions to Azure DevOps from a first pipeline, then map this first JSON to transform it for another Control-M Server that will be on another environment. I have a second pipeline that does that, and a third one that sends it again to the new Control-M Server but with the JSON mapped to the variables of the new environment, allowing us to have something fairly automated with Control-M.

We have financial chains and indeed logistics chains; for example, at Rexel, there are chains like Kinsale, which is really pure logistics. We have financial chains with various applications and various domains.

Control-M supports our DataOps and DevOps initiatives primarily through its integration with Azure DevOps, which allows us to map JSONs between environments and, for the CNAM client, it has been useful alongside Kubernetes to run processing in containers.

For orchestration across multiple environments, I use Control-M with Azure DevOps to do environment mapping, allowing us to have identical chains whose values differ based on the mapping file I've specified.

Control-M positively impacts our organization through easy-to-follow scheduling, thanks to the graphical interface. Another point is the Control-M API, which really makes it possible to move from one environment to another with DevOps tools, facilitating collaborative work because each application team that wants to move its job chains from one environment to another can do so automatically.

Regarding time savings, the Control-M API has really allowed us to quickly map chains from one environment to another, leading to significant time savings because we do not have to rework the JSON manually. As for errors, we use EasyVista in parallel for monitoring, and we also have ELK, which allows us to quickly visualize job executions, contributing to considerable time savings which I could not estimate exactly, but it is quite significant.

Control-M helps business users gain visibility and control over their processes with its interface, which centralizes all users and allows collaboration between different teams that are not necessarily from the same company. For me, it is the best tool currently available.

The best features that Control-M currently offers include basic scheduling, along with all the plugins in parallel that enable connections to various applications, whether databases, VMware, etc. The main strength compared to other schedulers really is all of these plugins that work alongside Control-M.

The plugins I use the most are the database plugins because I have a lot of databases, whether at Europcar or at Rexel. I also use the VMware plugins to restart VMs using VMware jobs. I currently use JMS too, but I have learned that unfortunately Web Services Java will reach end of life in 2027. I have seen that there are quite a lot of plugins available, for example for SAP, Denodo, etc.

Regarding the governance and security offered by Control-M, security is very effective in the sense that I can install certificates, whether in zone 1, zone 2, zone 3, or LDAPS.

I mostly use Control-M's policy management, approval, or risk-limitation capabilities during maintenance periods, especially when I have to restart Control-M, and I have used Workload Policies for that. For security, I regularly use LDAPS with roles.

Integrating Control-M with our DataOps and DevOps technologies is currently quite easy. As I mentioned earlier, I use Azure DevOps in parallel with Control-M to manage the different chains between environments and to handle mapping of variables and values between environments.

In terms of governance, Control-M has its own audit features that allow me to track who modified what and when, especially with job history.

Aspects of Control-M that could be improved today include maintenance. Currently, upgrades are done in completely different ways from one version to another, and there can be .zip files, .tar.gz files, or .Z files. Every time I have to upgrade Control-M, there are a lot of prerequisites instead of just having a single package that would do everything.

For improvements to make on-prem deployment or maintenance easier, I would recommend having one single package that handles deployments with no separate .zip files for patches and no different formats. Everything should be done from a single package that performs the entire upgrade itself.

I have been using Control-M for a little more than 6 years. I started first with the first client, which was Europcar, which ran on average around 5,000 jobs daily. Then I used Control-M for CNAM, where we ran around 150 jobs per day. Finally, I have used Control-M for Rexel, which is the client I am currently working on, where the number of jobs is around 2,000 for now, but it keeps evolving because we are doing a migration from Dollar Universe to Control-M.

Control-M is stable but can experience bugs. During maintenance, I have noticed that some patches did not clear certain data, especially logs, and on some commands, for example, PowerShell still appears in the output even though it had been removed.

Currently, I would say that for the current client, we have about thirty users of Control-M, around 30 to 40 people. For other clients, it is around 10 to 20, and these are mainly people who are either operators or administrators like me.

For now, I use Control-M regularly, but I do not plan to increase its usage in the future.

Control-M rates perfectly for scalability since I can add as many Control-M Servers and EMs as needed, and it also supports High Availability to maintain consistent performance.

Customer support for Control-M is very fast in responding, very effective, and takes into account everything I report.

For the current client, we are doing a migration from Dollar Universe to Control-M, as Dollar Universe did not meet all the requirements and needs we had.

I currently deploy Control-M on-premises because I prefer to manage it myself the way I want. At the moment, deployment is done by level 3 teams, which I am part of, and similarly, maintenance and upgrades are handled by our level 3 teams who do everything on-prem.

I try to install it such that the EM is on its own dedicated server, the Control-M Server on another dedicated server, and at least one Control-M Server per environment to ensure workload is handled as well as possible.

Currently, for the client I am working with, we do not have cloud and we do not use cloud. We are still on-prem, but for Europcar we started a migration to the cloud where we had some agents and a Control-M Server in the cloud to move chains one by one from on-prem to the cloud.

For the various clients I have worked with, at Europcar we were just two people. At CNAM we were two as well, and currently for the current client we are about three to four.

Time savings from Control-M comes from its DevOps capabilities that allow for mapping and automating the promotion of chains from one environment to another, along with time savings from all the plugins that prevent custom development for managing databases or VMware.

The sales teams on the client side handle that. I know that for the first client, licensing was based on the number of daily jobs limited to about 5,000 jobs per day. For the second client CNAM, it was limited to 700 jobs per day, and for the current client, the license is based on the number of agents, about a hundred agents across all environments.

For the current client, there was a competition between Control-M and VTom, and in the end, Control-M won because of its additional features, particularly the plugins and visualization that convinced the client.

Control-M is currently a very effective tool that has a lot of features, but there are still some points for improvement, particularly regarding Control-M upgrades, and I think additional features would be needed for certain specific applications.

I would like to install Control-M's AI, and I am currently studying how the installation is done and what impact the AI might have on Control-M. For now, I would like to test it only in non-production to see what it might do in production and to ensure that the AI does not go beyond what I ask it to do.

For those considering using Control-M, my advice is to get proper training because it is a very effective tool with many features, so best practices and consulting documentation and the Control-M community are highly recommended.

Control-M did consolidate job scheduler tools in my organization by pulling it all in one place.

What I like the most about Control-M is the task management, specifically the ability to integrate with multiple products and successfully complete repetitive tasks with ease.

It is easy to integrate Control-M with other technologies as my DataOps and DevOps processes change.

Control-M is excellent for building, scheduling, managing, and monitoring production workflows, but I would not use it for monitoring purposes.

The feature I would like to add is related to the licensing model, which I find challenging. The licensing model is based on the number of repetitive runs rather than the number of tasks. Sometimes it is difficult to understand how many runs we may have in a particular requirement.

I have been using Control-M overall for the last year.

Control-M is pretty scalable based on what we are currently using. However, what we intend to do is try to reduce its application and use it more effectively rather than scaling it.

Regarding technical support, we have a support contract, and when we reach out to them, the response is always very fast. Whatever we need gets addressed quickly.

Before Control-M, we were using Ansible.

The initial deployment of Control-M was fairly easy. I don't exactly remember how long it took the first time, but I think it was set up entirely in two hours.

I have used alternatives, but nothing comes close to Control-M because the advantage that Control-M has is the ability to convert business applications data integration into other IT-related solutions that we use. In other cases, we would have to use multiple middleware. There are products evolving in the market that we are seeing that could be utilized here, but nothing is completely replacing Control-M fully. We really have to look into custom development to plug those gaps.

I have been working in my current field as a professional for the last 18 years.

Control-M does require maintenance on my end. The overall server management involves ensuring that patches, vulnerability validations, and related tasks are properly handled because we have it in an on-premises deployment. Many deployments need this level of attention when it comes to patch management and vulnerabilities. Those are the types of maintenance and version controls required.

I would give Control-M an overall score of 8.5 out of 10.