Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
N/A
Control-M
Score 9.4 out of 10
N/A
Control-M from BMC is a platform for integrating, automating, and orchestrating application and data workflows in production across complex hybrid technology ecosystems. It provides deep operational capabilities, delivering speed, scale, security, and governance.
$29,000
per year
Jenkins
Score 8.4 out of 10
N/A
Jenkins is an open source automation server. Jenkins provides hundreds of plugins to support building, deploying and automating any project. As an extensible automation server, Jenkins can be used as a simple CI server or turned into a continuous delivery hub for any project.
Using Jenkins and Kafka, it is not for the same purpose, although it might be similar. I would say AirFlow is really what it says on the can - workflow management. For our organisation, the purpose is clear. So long your aim is to have a rich workflow scheduler and job …
Jenkins is a user-friendly tool, but it doesn't offer the same blend of features that Control-M has. Often, Jenkins jobs become stuck, but Control-M typically doesn't; instead, it generates an alert to the designated email recipient. In the case of production, this act is so …
We need end to end automation, so using Control-M we can achieve this but by using Azure Automation it automates data pipelines across the cloud. Compared to Autosys, Control-M has more advanced and modern UI and simple job life cycle management. When compared with Apache …
It supports both on-prem and cloud environments. Easy to handle complex, multi-system workflows more efficiently. Helped reduce manual intervention, which has decreased errors and operational costs.
Control-M stands out in the workload automation space for its robust orchestration capabilities, especially in complex, hybrid IT environmentsActiveBatch: Known for its rich integrations and intuitive workflow designer,
ActiveBatch often scores higher in ease of use and …
We have been using Control-M for almost 20 years and our teams are already trained to use it. Other tools are not as robust and resilient as Control-M. Control-M's graphical interface is very easy to use.
We would still use Control-M as overall orchestrator to manage the workflows created in these other products. We do not consider them as a replacement for Control-M.
It is a very innovative and feature-rich solution and can be used to complete many diverse tasks and solve different issues, resulting in significant time savings and cost-effectiveness.
Since IBM Workload Automation isn't compatible with all of our project's operating systems, we've decided to switch to using Control-C instead. Second, the provider provides top-notch customer service in the event of an emergency. Control-M is a lightweight program that places …
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
Anytime you have a process that has to do multiple things, transfer data, interact with other systems, Control-M is critical. Not only does it provide the insight to what is going on, but it also lets you keep tight audit controls over access, reduces the need to spend large amounts of time tracking down issues, reduces the need to write custom "code" to do integrations with other systems and helps you better manage and track critical SLAs for workflows across the business.
Jenkins is a highly customizable CI/CD tool with excellent community support. One can use Jenkins to build and deploy monolith services to microservices with ease. It can handle multiple "builds" per agent simultaneously, but the process can be resource hungry, and you need some impressive specs server for that. With Jenkins, you can automate almost any task. Also, as it is an open source, we can save a load of money by not spending on enterprise CI/CD tools.
Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
The good thing is that there are so many connectors available. Control-M provides lots of features, and we are using almost 60 to 70% of them. Control-M is providing us with so much capability to use during our daily problem-solving.
Most of the job creation is very simple and quick and worked as per expectations.
Testing and debugging are also very easy, and you can test multiple scenarios using temporary changes during job runs.
Log and output presentations are also very good, short and detailed.
To monitor specific job net, we can create viewpoint, which can be use on daily basis.
Automated Builds: Jenkins is configured to monitor the version control system for new pull requests. Once a pull request is created, Jenkins automatically triggers a build process. It checks out the code, compiles it, and performs any necessary build steps specified in the configuration.
Unit Testing: Jenkins runs the suite of unit tests defined for the project. These tests verify the functionality of individual components and catch any regressions or errors. If any unit tests fail, Jenkins marks the build as unsuccessful, and the developer is notified to fix the issues.
Code Analysis: Jenkins integrates with code analysis tools like SonarQube or Checkstyle. It analyzes the code for quality, adherence to coding standards, and potential bugs or vulnerabilities. The results are reported back to the developer and the product review team for further inspection.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
I haven't come across too many spots where I'm not happy with the product. Most of the shortfalls were in my knowledge of the product as opposed to the actual product. Currently we're having a little bit of an issue with the deployment of the software to the servers, but it's more of an "us" problem than a product problem. I can't really give any good examples of shortfalls of the product that I've found so far.
It is one of the best solutions on the market, in terms of innovation, reliability and stability. Control-M provides security when used by the largest companies in Mexico such as banks, department stores and logistics. It has proven to be able to integrate with new technologies on the market and provide almost 100% availability, thanks to its automatic FailOver scheme.
We have a certain buy-in as we have made a lot of integrations and useful tools around jenkins, so it would cost us quite some time to change to another tool. Besides that, it is very versatile, and once you have things set up, it feels unnecessary to change tool. It is also a plus that it is open source.
For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.
User experience is meeting my expectations. We had a manual checklist, which Control-M Reports has now replaced, that helped us check the jobs without any issues. So, being fair with the work, the ratings should also be fair. More to come as the AI progresses; this will not only help motivate the Control-M Developers but also lead to the development of advanced technology.
Jenkins streamlines development and provides end to end automated integration and deployment. It even supports Docker and Kubernetes using which container instances can be managed effectively. It is easy to add documentation and apply role based access to files and services using Jenkins giving full control to the users. Any deviation can be easily tracked using the audit logs.
Secondary Instances: Control-M supports the installation of a secondary instance of the entire Control-M environment, Control-M/EM, or Control-M/Server.Automatic & Manual Failover: In case of a failure on the primary host, Control-M can automatically failover to the secondary host if using Oracle or MSSQL databases. Manual failover is also an option, enabling a controlled switch during planned maintenance.Fallback: After resolving the issue on the primary host, you can easily fall back to it, or even designate the secondary host as the new primary. Database Replication: For high availability, Control-M leverages database replication from the primary site to a disaster recovery site. While replication is essential, its implementation and maintenance are the user's responsibility.
good page load times, efficient report completion, and minimal impact on integrated systems. Specifically, the well-designed GUI contributes to a positive user experience, and the platform's ability to automate various stages of the workflow, including Big Data processes, is highlighted as a key strength. Fast Page Loads: Control-M is reported to have a responsive user interface with fast page load times, allowing users to quickly navigate and manage their workflows
No, when we integrated this with GitHub, it becomes more easy and smart to manage and control our workforce. Our distributed workforce is now streamlined to a single bucket. All of our codes and production outputs are now automatically synced with all the workers. There are many cases when our in-house team makes changes in the release, our remote workers make another release with other environment variables. So it is better to get all of the work in control.
Support is generally excellent. Getting lower priority ones resolved can take a while, but it's rare for something to have to be dumped in the "unfixable" bin. If you end up speaking to Houston or Tel Aviv, then you know you've got a "live one".
As with all open source solutions, the support can be minimal and the information that you can find online can at times be misleading. Support may be one of the only real downsides to the overall software package. The user community can be helpful and is needed as the product is not the most user-friendly thing we have used.
Very knowledgeable instructors provide a hands-on, collaborative learning experience and can interact directly with instructors to develop our Control-M skills. This format allows for immediate feedback, in-depth discussions, and tailored guidance, leading to a deeper understanding of Control-M concepts and practical application. Face-to-face interaction fosters higher engagement and a more dynamic learning environment.
Simple and easy to use web based, well paced. Available any time. All online courses are simple and easy to access and use. Very practical everyday use scenarios and solutions. Incorporates software simulations, learning games, and built-in assessments to enhance comprehension and engagement. Online subscriptions are regularly updated with the latest product information, ensuring users have access to the most current knowledge.
As HA we have depend on the external DB, why don't we have HA feasibility with embedded DB. As with external DB, there are performance issues and fine tuning the DB. As if its embedded DB, Control-M it self take care of the functionality.
It is worth well the time to setup Jenkins in a docker container. It is also well worth to take the time to move any "Jenkins configuration" into Jenkinsfiles and not take shortcuts.
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
Control-M: Known for its comprehensive workload automation capabilities, handling complex job scheduling, dependency management, and IT process automation. TWS: Traditionally strong in Batch processing and job scheduling, focusing on high-performance computing environments. TIDEL: Offers a combination of workload automation and IT process management, often used in mainframe environments.AutoSys: Provides job scheduling and workflow management with a reputation for scalability and performance.
Overall, Jenkins is the easiest platform for someone who has no experience to come in and use effectively. We can get a junior engineer into Jenkins, give them access, and point them in the right direction with minimal hand-holding. The competing products I have used (TravisCI/GitLab/Azure) provide other options but can obfuscate the process due to the lack of straightforward simplicity. In other areas (capability, power, customization), Jenkins keeps up with the competition and, in some areas, like customization, exceeds others.
While Control-M offers flexibility with usage-based and subscription-based pricing, some users might prefer more predictable, upfront costs, especially for large-scale deployments. A potential area for improvement could be offering more options for fixed-term contracts with predictable pricing based on factors like the number of agents or jobs, providing a clearer budget for long-term planning
awesome product.Control-M delivers advanced operational capabilities easily consumed by Dev, Ops, data teams, and lines of business.Control-M Workflow InsightsApplication and data workflow observability: Increased confidence that SLAs are being met for Control-M users and IT leadersComprehensive control and management capabilities: Enhanced dashboards and reporting with constant telemetry and intelligent analysis on executing workflowsSelf-service visibility: In-depth reporting to help teams work autonomously
Strengths: The vendor provided strong post-sales support, timely issue resolution, and effective onboarding. Their technical team was knowledgeable and responsive, ensuring smooth integration and minimal disruption. Training resources and documentation were comprehensive. Areas for Improvement: While overall service was excellent, occasional delays in advanced customization or escalations slightly impacted timelines. More proactive optimization suggestions could further enhance value.
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
Since centralizing all our workflows in Control-M, we've cut end to end processing time by nearly 30%
Before Control-M we were babysitting scripts, manually rerunning failed jobs, and chasing ghost errors. With automated recovery, smart notifications, and fewer failures slipping through the cracks, we have saved 3 hours a day across teams
Our workflows success rate sits at 99.95% and when things do fail, they are pinpointed immediately
