I have been using Control-M for more than six years. Initially, it was mostly just monitoring the jobs, but now I also do some troubleshooting around that.

My main use case for Control-M these days involves multiple jobs running in our contact center systems. We have multiple nodes to begin with, and some of them are responsible for maintaining the predictive dialer calling list for records sourced from multiple platforms. Along with this, we also have certain jobs deployed for our reporting purposes, where our databases are synchronizing with other Genesis databases. Additionally, we have multiple log archiving systems or jobs that have been deployed as well. We have some ServiceNow jobs that trace and manage the employee profiles, and then we have some speech-related Nuance jobs scheduled as well.

One of the major use cases of Control-M that we use is our log archival process. This process integrates file movements with job scheduling and enables secure file transfer by using both FTP and SFTP file transfers. It triggers the job when the file arrives, and then it also validates the file completion and size before actual processing. So, in the contact center cluster, one of the jobs that we have is the Informat job that extracts the caller data from Informat and transfers it to various downstreams such as BIH or Connect Direct. Apart from this, we also have various SQL stored procedure purging jobs in Genesis, and there is one main, important Cassandra job that runs on the Cassandra nodes, selected for incremental backing up. The Pulse housekeeping, where the job runs and cleans the ECP snapshots every 30 minutes, is one of the major, significant jobs that we use. Along with this, we also have a cyclic job that runs every 15 minutes on each of the MCP nodes. Every 15 minutes, it resyncs the job, basically for the audio file resyncing that happens from one of the applications to a given directory. This means the most recent file that has been uploaded is put into all the MCP boxes every five seconds, and then the right announcement gets picked for the user to hear.

The log job archival basically copies and archives all the Genesis log files for a period of retention given. It logs the files from site one to a specific site location and site two to another specific site location. This is not only in production; it is for all environments including Dev, SIT, and QA that we have. We have also automated that all archived log files older than three days are gzipped, and all these files will be moved to a different archive location than the location that it has initially been sent to. It also makes sure that we are masking and the schedules are followed, which are not getting archived.

The best features Control-M offers that make all this possible for me include the job scheduling, which is most importantly critical. It enables us to schedule jobs across multiple platforms such as Unix and Windows together, and also the jobs running at very specific times help eliminate a lot of manual task execution by triggering based on either a file arrival or even a system event. It also enables us to run the jobs in the right order. Along with this, we also have the data pipeline and ETL automation, which helps various data engineering and analytic teams automate the Hadoop jobs and trigger downstream analytics after the data ingestion. All the ETL processes are managed better in terms of both data validation and quality checks. Additionally, the business-critical processes meet deadlines, for example, the ServiceNow data that we have to receive before 8:00 AM in the morning, or the month-end or quarter-end batch runs that need to happen, are done in a timely and accurate fashion.

The job scheduling and sequential jobs have been the most important feature of all. The rsync specifically, where the cyclic jobs run every 15 minutes without any manual intervention, makes sure that the process is streamlined and does it without any manual intervention, which helps a lot.

Along with this, end-to-end workflow orchestration, which is basically event-driven or file-driven, differentiates Control-M from any other basic schedulers. It is not just about running a job on a schedule, but it also enables complete business workflow from an application to multiple platforms and multiple environments. Dependency-based execution ensures that the previous job or the upstream job has completed before starting with the event, and multiple other conditions can also be set. The cross-technology enablement allows one workflow to span across multiple systems, from cloud services to databases to Unix and Windows, providing a single point of control for everything.

Control-M is a very nice product that is practical, but it is challenging to understand how certain features work. The UI and user experience sometimes feel complex and can be simplified a little bit to provide cleaner dashboards. The major complexity is the licensing complexity and access-related challenges.

Simplifying the UI can provide us better use of the application itself. Probably some more documentation around how to use the schedules or the alerting systems would also be helpful.

Control-M systems have been stable even during upgrades and patches, with very minimal disruptions to the system, so it has been stable throughout.

We started using Control-M with very few teams starting with about 50 users, but now we have about 3,000 plus users using Control-M in my organization.

Control-M customer support has been good, but we have not had the opportunity to extensively talk to them because we have an in-house support team that we reach out to before contacting the actual BMC vendor.

The other alternatives that we previously used were mostly cron jobs and other system jobs. We briefly used IBM workload automation but did not proceed with that. We also used Jenkins with some plugins, but ultimately, we did not pursue alternatives such as AutoSys. I believe Control-M is hard to replace.

The organization explored AutoSys and IBM workload automation before ultimately choosing to go ahead with Control-M.

We have a team of 25 to 30 members who are responsible for the deployment and maintenance of the Control-M setup. Our team includes architects and designers as well as deployment and support personnel.

I see a return on investment with Control-M. The other challenge we currently face is that they have started charging us, which is more of an enterprise-level decision, as they began charging us for each job run we have.

I do not have a major role in terms of pricing, setup cost, and licensing. Our team was only not allowed to access Control-M for a certain duration due to licensing constraints, which I feel is a challenge, but I was not directly involved in any of these pricing, setup, or licensing related discussions.

The impacts that Control-M has caused for my organization have very visibly increased operational reliability. Before Control-M, most jobs were script-based, such as cron jobs, and there was a lot of dependency on manual monitoring. Until the jobs were reported as failed by the business teams, we would not have had visibility over them. Now with Control-M, we have an end-to-end workflow which is centrally managed. If a node has failed, it sends notifications, and there is a lot of error handling built in. There are multiple automatic retries, reducing human intervention. In terms of issue detection and resolution itself, we have dashboards configured that enable us to get alerted even before the businesses are impacted or the businesses report the impact, allowing us to solve issues proactively. This has also increased productivity improvement.

When one of our reporting downstreams processes data and uploads it to our systems, it used to take an hour for the data to actually reflect. Businesses would notice missing data in the systems when they consumed the data. Now, within the duration when the job runs, it counts the number of rows we have, which means if the job fails, it is notified immediately within that 15-minute duration, helping us rerun the job. This means issues that were reported in an hour's time now get reported within the duration of the job running, which is within 15 minutes, leading to a significant improvement in how we see that the reports are being run.

There is a huge user base in our organization, with about 3,000 users using Control-M. The levels of usage vary; some have read access and just view the jobs, while others perform deployments in terms of job scheduling and other tasks.

We extensively use Control-M to schedule multiple banking-related jobs in varied fields, not just the contact center. We definitely intend to increase the usage.

The biggest lesson I have learned from using Control-M is that it is a best-in-class workload automation platform, effective in building, scheduling, managing, and monitoring complex workflows, especially for critical applications such as DataOps and enterprise DevOps environments where reliability and SLAs play a major role. The cross-system orchestration matters significantly more than speed alone, as it ensures jobs run accurately and efficiently.

My advice for others looking into using Control-M is that no matter how many systems you have, Control-M is the most competent and enterprise-scalable tool available. With various requirements, it is extremely reliable in monitoring and scheduling, making it an excellent choice. I would rate Control-M an 8 out of 10 overall.

In my previous project, we were using Control-M, and we automated the data pipelines using SQL Server Agent jobs and created the Databricks workflow. We had some data available in SQL Server and some in Databricks, and because we had two systems, the orchestration process was completely different, and we were not able to manage or create a dependency because both tools were different. That is why we implemented Control-M in the past project and automated all the SQL Server jobs and the Databricks workflow using Control-M. By using a single platform, Control-M allowed us to create a dependency between the SQL Server and Databricks data. On the reporting side, we were using the Tableau dashboard as well, and for Tableau, we were using the extract to display the data. We were refreshing the Tableau extract using Control-M. In my last project, overall all the data pipelines including the Tableau extract refresh were done using Control-M.

We expanded a lot because previously we were using multiple tools for the same orchestration purpose, such as Databricks workflow and SQL Server Agent. Now, we are using the same product or a single tool for multiple tasks, which is very helpful for developers as well as business stakeholders.

I appreciate Control-M because of the dependency it offers. As I mentioned, we had some data available in SQL Server and some in Databricks, and it was hard to create a dependency when we were working on different tools. That is why we chose Control-M so that we could create a dependency, and we had some highly critical banking data in that project. The SLA was very minimum, and we had to get the dashboard refreshed every morning at 7:00 a.m. Due to the SLA features in Control-M, we chose it in the last project.

I find that Control-M provides a single UI platform where I can monitor all the jobs. Previously we had different jobs, so we had to monitor each job individually. With Control-M's single platform UI, we can monitor all jobs. The main benefit is that Control-M has a retry functionality, so if any job fails during execution or due to bad data quality, we can retry the job. Once we receive the data, the job can execute automatically. The alert mechanism also triggers emails to business stakeholders whenever any job fails. These are the main features I prefer about Control-M.

Previously, we set up alerts so that whenever there was a delay in the file, it automatically sent alerts to business stakeholders indicating the file's unavailability. Whenever there was a delay, it triggered an email to notify that we were expecting the file at a certain time. Additionally, we set up a file-based trigger. Since the time of file arrival is not consistent, we configured the job to execute automatically when the file arrives, ingesting the data into our final database. This file-based trigger was a key feature we explored.

I think the pricing is a factor, and it is high. I am currently working in a multinational company that has purchased the premium enterprise-level license for all developers, so it is not a big deal for our project. However, someone in a small company or startup might face pricing constraints while implementing Control-M, as the pricing seems a bit high compared to other tools such as Airflow.

One area for improvement in Control-M could be pricing, and another is the learning curve. I feel that when someone starts working with Control-M, they need at least one month to onboard and understand all the features. Although documentation is available, understanding all features takes time. Another recommendation would be for UI improvements, as I felt the UI seemed outdated.

I feel that it is a little bit difficult to integrate Control-M with technologies for DataOps and DevOps processes, especially initially, as I needed about one and a half months to understand the complete features and flexibility of this tool. From a developer standpoint, it is not very user-friendly. However, once I become skilled in this tool, it provides great flexibility.

I have been working with Control-M for six plus years.

I faced a performance issue once because we created a very large data pipeline with multiple dependencies in Control-M. So, we narrowed down one workflow into multiple sub-workflows, which improved performance. Processing a large amount of data can be complex and time-consuming.

I can raise a support ticket for BMC software whenever I have any technical issues, and they respond within a three-day SLA, providing full support.

I would rate the tech support of Control-M as 8.5.

I evaluated Airflow before choosing Control-M. In Airflow, we faced a similar situation because we had to create different cron jobs for each Python script. We had 100 plus Python scripts fetching data from multiple source systems, and in Airflow, creating dependency between each cron job was very hard. That is why we switched from Airflow to Control-M.

The initial setup process was done by our infrastructure team. I worked as a developer to create jobs, but the actual setup was quite good and well-supported by BMC software.

Our initial setup was completed with full support from the infrastructure team. After that, the workflow creation and job creation in Control-M were entirely managed by our developers.

The initial setup process was done by our infrastructure team. I worked as a developer to create jobs, but the actual setup was quite good and well-supported by BMC software.

Our initial setup was completed with full support from the infrastructure team. After that, the workflow creation and job creation in Control-M were entirely managed by our developers.

I think the benefit is very high. If a company does not have any budget constraints, they should definitely explore Control-M because it allows for end-to-end orchestration of the project without needing separate projects for the data pipeline and downstream applications such as reporting. All tasks can be accomplished using one product, providing significant value if budget constraints are not an issue.

I find it cost-effective, but I am not fully certain about the overall ROI.

The biggest lesson I learned from using Control-M is that it provides a single UI to monitor all jobs, making it much easier compared to my current project where I use Airflow, which involves managing multiple cron jobs across different tabs.

We do not have any direct contact with BMC software, so I would not describe the relationship as transformative.

I rate Control-M as nine because it simplifies complex data structures and pipelines.

I am currently working as a Data Engineer at Cognizant. I have been using Control-M for the past eight months since I joined Cognizant as a Data Engineer. As a Data Engineer, my job is to monitor jobs and maintain pipelines, and Control-M is a scheduler tool which we use to schedule jobs by linking the jobs as predecessors and successors so that the flow of the data pipelines continues without human interference.

The daily important task which we are monitoring is the SaleRPT report, which gives business users the sales that happened the previous day in a restaurant at our project in Cognizant. The jobs are connected in such a way that starting, there are replication jobs, and then they are connected to SQL Server to transform the data and load it into Oracle SQL. From there, again, the data is loaded into our data warehouse tables, and the final target tables are Essbase. So this total flow has around 17 to 18 jobs which are scheduled to run twice a day when we get EOD clearance for each site. So these are the latest tasks for which I used Control-M to schedule jobs in a sequential manner.

In our legacy system, there are some Informatica jobs and some SnapLogic jobs. For example, there are three sets of jobs which are from Informatica, and the next successor jobs are from SnapLogic. Control-M allows us to link these Informatica jobs to SnapLogic. If the Informatica job is completed, it would automatically trigger the SnapLogic pipeline. So it allows the usage of multiple tools. For DataOps and DevOps, it is quite important to use Control-M, as it is a scheduler which schedules multiple jobs based on our requirement. We can easily change the schedule for a particular day if we have a lesser number of data. And if there is any data miss, we can also easily reprocess using Control-M by putting a few jobs on hold and running the jobs manually. So I think it is quite extensively important to use Control-M for a Data Engineer at any level.

There are multiple teams which are using Control-M. I think there are nearly 80 to 90 employees who are using Control-M tool in my organization in my current project at Cognizant. Mostly, 60 to 70 percent of them are Data Engineers. Some are from the BI ETL, Business Intelligence ETL team, and some are from the DevOps team, and some are part of the development team also. And some are part of the Aloha Insight team. These are the teams which I know which are currently using Control-M.

I have been using Control-M to monitor and maintain pipelines. It helps us schedule jobs by linking them as predecessors and successors, ensuring the continuous flow of data without human interference. Control-M is the most used tool in my current project and is essential for job scheduling and checking job failures. Its easy interface makes it beginner-friendly.

Control-M's ability to link jobs from different tools such as SnapLogic, Informatica, and GCP DAGs enhances its functionality. The scheduler, ad hoc runs, and job linking features are particularly useful. It allows job connections to various tools and notifies us via email of any job failure, providing logs for quick rectification.

It can save us significant time, reducing errors and the time taken to rectify them. Automatic failure notifications enable rapid response, facilitating efficient job management. Control-M enables development on various platforms, which is essential for DataOps and DevOps operations.

Its user-friendly nature allows quick learning and management of tasks, with significant time savings compared to manual processes. We now receive automated failure notifications, which streamline error rectification and job reruns. Control-M's integration with Informatica and SnapLogic further exemplifies its efficiency.

One thing I find challenging is if a job is executing and we put it on hold, then if a job is an Informatica or SnapLogic job and we put it on hold, the corresponding pipeline in Informatica or SnapLogic would still be executing. We need to again go to that tool and kill the job. Rather, it would be easier if we kill the job in Control-M and it would automatically be killed in Informatica or SnapLogic.

In some cases, some jobs go into a waiting state. So again, we need to change the Control-M settings for that particular job manually to transform it into the normal flow. These are the two things that if they are changed, Control-M would be an even better tool.

I have been using Control-M for the past eight months since I joined Cognizant as a Data Engineer.

We have never experienced any licensing or any security issues from Control-M. My manager and the other members of my upper hierarchy manage the pricing. Since I have been using Control-M for the past almost one year, I have never experienced any security or software issues in it.

Control-M is easily scalable. I would rate it a nine out of ten when it comes to scalability of Control-M.

I have not used customer support until now, as the monitoring and the management of Control-M is done by another team. However, the other team which currently manages Control-M has helped us a lot.

When I was deployed into this project, Control-M was already in use, so I have not chosen or compared Control-M with other tools. Since I have been using it, I have not experienced any flaws or any issues.

For development, maintenance, and changing, I think around four to five people are enough for monitoring. For development, we need quite a lot of them. Once it is developed, only three to four people can easily manage Control-M.

I would recommend Control-M to most people. When it comes to metrics, I am not sure on how much the tool has saved us, but I am quite sure that it saved us a lot of time.

For scheduling, Control-M is the first tool which I have used. Along with Control-M, I am also using DAG monitoring, which is already enabled in GCP, which is almost similar to a scheduler.

We can easily depend on it to schedule the jobs and monitor them. I am already using it quite much for my daily tasks for my project. I am satisfied with the way I am using it and the features it is allowing me.

One thing is how easy it is to use. Anyone, if they open Control-M and look at the jobs, they can easily know how to run a job, how to kill a job, how to put it on hold, how to check the logs, when it started, when it ended, whether it is running fine, or if there are any anomalies in the job. So I would recommend it. I advise them that it is a good tool. I would rate this product an eight out of ten.

I have been working with Control-M for about six months as part of my role. During this time, I have mainly used it for monitoring and managing batch jobs and automated workflows.

Our main use case for Control-M is managing and monitoring batch workflows across different systems. We use it to schedule jobs that run scripts, database processes, and ETL-related tasks. We make sure they execute in the correct order based on dependencies. On a daily basis, I mostly work with monitoring job runs, checking job statuses, and troubleshooting failures when a job does not complete successfully. We also review logs, rerun jobs when needed, and make sure the workflows complete within the expected time windows. Control-M helps centralize all this so we can track and manage automation more efficiently instead of handling tasks manually.

One of the examples for centralized monitoring in Control-M is the ability to view the status of all the scheduled jobs from a single dashboard. Instead of checking multiple systems individually, we can see whether jobs are running, completed successfully, or failed in one place. For example, if a job that runs a database script fails during the night schedule, we can quickly identify the failure from the monitoring interface, review the logs, and rerun the job if needed. This helps the team respond faster and keep the workflow running smoothly.

One of the best features of Control-M is its ability to manage complex job scheduling and dependencies across different systems from a single platform. It makes it much easier to automate workflows and monitor job execution in real-time. The centralized monitoring and alerting help us quickly identify failures and take action, which improves reliability and reduces manual effort.

Control-M has had a positive impact by improving the automation and reliability in our batch processing workflows. It helps ensure that jobs run in the correct sequence and reduces the need for manual intervention. The monitoring and alerting features have also made it easier to detect failures early and resolve issues quickly, so it helps keep our scheduled processes running smoothly.

One area that could be improved is the user interface. While the platform is very powerful, the UI can sometimes feel complex for new users, and it may take time to become familiar with all the features. Improving it and making the navigation more intuitive would help teams adopt it more quickly.

Another improvement could be simplifying the initial setup configuration process for organizations that are implementing Control-M for the first time.

I have been employed for almost four years, approximately three years and ten months.

Control-M is definitely stable. In my experience, once workflows and jobs are properly configured, it runs reliably and handles scheduled processes consistently. Most issues we encountered are usually related to the jobs themselves rather than the platform, and the monitoring tool helps identify and resolve them quickly.

Control-M is quite scalable in my experience. It can handle a large number of jobs and workflows across different systems without major performance issues. As the workload grows, it is possible to expand the environment and manage additional processes while still maintaining centralized monitoring and control.

Customer support has generally been good. The support team is responsive and provides helpful guidance when issues arise. The documentation and knowledge base resources are also useful for troubleshooting common problems.

Before using Control-M, some workflows were handled through basic scheduling tools and manual scripts. Moving to Control-M helped centralize job scheduling and monitoring, making it easier to manage dependencies and automate processes more reliably.

The biggest lesson I learned while working with Control-M is the importance of properly defining job dependencies and workflows during the initial setup. When dependencies are clearly configured, the automation runs smoothly and requires less manual intervention. It has also highlighted how valuable centralized monitoring is because it allows teams to quickly identify and resolve issues before they impact downstream processes.

We are just a user.

For return on investment, we have experienced improved automation and reduced manual effort. In terms of operational efficiency, automation through Control-M has reduced manual overhead by around twenty to thirty percent, especially for routine batch job monitoring and scheduled tasks.

I have no idea how the pricing, setup, and cost licensing is done. It is done by the finance department.

We saw the working methodology of Control-M, how batch jobs are handled, and how the automation works, so we just went for Control-M.

With job scheduling and workflow automation, this automation has increased the scheduling time by fifty percent. The monitoring task has been reduced by twenty to thirty percent. Instead of going on multiple tabs, we can view it at once. Workflow management through the technologies is a bit of a complex task. As we have used this, we can implement it. For new users, it might be a bit complex.

Currently, we have been using this for the past six months. We are seeing good, positive results. The automation workflow is also good, and the batch scheduling jobs are definitely good. We will still want to try it on different platforms and then decide on any further usage or increase in usage of Control-M.

In production, this workflow is mainly through the monitoring and reporting features in Control-M. We check the job status to make sure the scheduled process completes successfully within the expected time window. If the job fails or is delayed, we review the logs again, analyze the dependency chain, and rerun or troubleshoot the job if needed. This helps ensure that the overall production workflow continues without impacting downstream processes.

One piece of advice I would give is to spend time planning the job dependencies and workflows carefully during the initial stages. If the workflow is well-structured, Control-M can automate processes very efficiently and reduce manual intervention repeatedly.

Overall, Control-M has been a reliable solution for managing automated workflows and scheduled jobs. It provides good visibility into job execution and helps teams maintain operational stability. I gave Control-M a rating of eight because it is a very reliable solution for scheduling jobs and automating workflows, and it helps me manage complex job dependencies and provides good monitoring capabilities, which makes it easier to track and troubleshoot batch processing. The reason I did not rate it higher is that the interface can feel complex for new users, and the initial setup and learning curve could be improved. With some improvements to the user interface and onboarding experience for new users, it could become even more effective.

In our project, we are using Control-M for job scheduling and monitoring. We have data workflows and many other components that we can manage from a business point of view. We can manage processes across on-premises and all kinds of environments.

Control-M is the easiest tool available because we can accomplish what we want. We can automate processes and reduce manpower, which is the primary benefit. We can manage all workflows across different cloud environments with the help of batch scheduling, automating, and controlling jobs. It is easy to handle if you are confident with scheduling and related components. We can improve Service Level Agreements and SLA management.

Integrations are available through API and Control-M automation API to build, run, and manage workflows. We can integrate with CI/CD pipelines. As an automation solution, Control-M provides cost and licensing benefits that are good for our ownership considerations. Flexibility is also available. Job failure monitoring includes email notifications and alerts. Some users feel that the interfaces, both web and desktop, could be more streamlined.

IBM workload automation is another tool, but we are satisfied while using Control-M and comparing it to other solutions. IBM is primarily suited for mainframe integrations only, whereas Control-M is a workload automation platform where we can implement job as code and use it easily.

Deployment and agent upgrades are straightforward with Control-M. If you want to upgrade one agent version or the client version, Control-M is easier to manage compared to other tools. If we have Java capabilities, we can easily perform these upgrades. Moving to Oracle 19c would be beneficial. TLS protocols are in place while fixing vulnerabilities. TLS 1.2 and higher versions are good, and we could upgrade to TLS 1.3 for better security.

From a security perspective, communication protocols like TLS are available. SAP optimization would be beneficial if possible. Improving the overall application path would enhance the solution further.

We have been using Control-M for four years.

We experience all kinds of stability issues, and they are difficult to manage.

Compared to all other tools, the scalability is moderate only.

We are receiving all the good support we need. Even when we encounter issues with vulnerabilities that we cannot fix internally, the vendor provides excellent response times and support. Everything has been positive.

We have used other vendors in the past, including solutions from Azure, AWS, and Salesforce.

We have achieved nearly 30% return on investment.

Nearly 100 users are using Control-M in our organization. We previously used BMC Eclipse, which is a Software as a Service solution, for three years. Control-M has enabled us to transition from mainframe to the cloud environment with Azure. We are using this on a video conference basis. My overall rating for Control-M is 8 out of 10.

I mainly use Control-M for scheduling jobs according to my requirements, which allows me to include holidays or exclude Saturdays, Sundays, or any specific time.

I also use cyclic jobs that run in a recurring period and can set up prerequisites before the job runs.

I can implement a File Watcher to establish a rule that a particular job can only run after the upstream job is completed.

I have created several new jobs using these capabilities.

Within Control-M, I use smart folders and jobs, and I can approach scheduling the way I want, so I have used it fully from start to end.

Control-M is straightforward for building, scheduling, managing, and monitoring production workflows.

I use the XML side for configuration, which requires importing, and I find it user-friendly.

Control-M has been used for building, scheduling, managing, and monitoring production workflows, especially at JPMorgan Chase Bank where most production support is monitored through Control-M.

I worked as a developer, developed jobs, and sent them to production, and the process was seamless.

My favorite feature about Control-M is the rule-based calendar feature.

The initial deployment of Control-M is straightforward, as it involves a simple installation of a Control-M agent within the server.

Control-M demonstrates strong scalability.

My application alone uses approximately 65 jobs, and for each folder, there are thousands of jobs that can load immediately in production.

When using a filter, the search is quick, and overall scalability has been excellent.

After migrating to Control-M, the positive effect on business-critical applications is evident: whenever a job ends successfully or fails, notifications are sent immediately.

These notifications go to EventBridge, alerting the team to any critical jobs.

High-priority jobs trigger alerts to engage developers immediately, ensuring there is no delay in response.

One area for improvement in Control-M could be in the logs, as they only show when the job started and whether it ended successfully or not.

Since the Control-M agent is running on the servers, we could pull up logs and display the actual error to aid in debugging.

I have been using Control-M for approximately eight to nine years in my career.

Regarding stability, I do not experience any lagging, crashing, or performance issues with Control-M.

Control-M demonstrates strong scalability.

My application alone uses approximately 65 jobs, and for each folder, there are thousands of jobs that can load immediately in production.

When using a filter, the search is quick, and overall scalability has been excellent.

I have never contacted technical support or customer support from BMC regarding Control-M because we have sufficient in-house expertise to share knowledge about it internally.

I started on TWS, Tivoli Workload Scheduler, which is an older tool with a poor user interface.

After transitioning to Control-M, I have been very satisfied with it.

I have not identified pain points until now because the use cases I addressed were fully satisfied by Control-M's capabilities.

Whatever use cases I needed, they were fulfilled by the product.

I used Tivoli Workload Scheduler earlier, but now the company has standardized on Control-M without using any other schedulers.

The initial deployment of Control-M is straightforward, as it involves a simple installation of a Control-M agent within the server.

Only one person is required for deployment, and it can be completed without needing a team.

For both job deployment and software installation, one person typically handles it, as we use an automation process with no manual work, making it a self-service operation.

I have personally used TWS as an alternative to Control-M.

I prefer Control-M over TWS.

The biggest difference between TWS and Control-M is that TWS has a poor user interface, while Control-M has an excellent user interface and alerting capabilities.

TWS requires monitoring on one page without navigation directions, whereas Control-M uses smart folders with parent and subfolders, maintaining a clear graph structure and allowing the creation of custom workspaces.

Regarding data pipelines, I have not explored integrating them via Control-M.

Non-technical users or business users do not need to use Control-M, as they primarily rely on developers or application support.

Currently, all employees at JPMorgan use Control-M for everything except mainframe jobs, which are monitored and developed through Control-M.

For maintenance, a patching team manages it, and I do not experience any downtime with Control-M.

I might not notice maintenance communications since they could be performed behind the scenes or during off-business hours.

I would rate this review an 8 out of 10.