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Control-M VS Apache Airflow

Compare Control-M VS Apache Airflow and see what are their differences

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  2. » Videos
  3. » Reviews
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Control-M logo Control-M

Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Control-M Landing page
    Landing page //
    2023-07-12
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Control-M features and specs

  • Comprehensive Job Scheduling
    Control-M provides an extensive range of job scheduling capabilities, supporting various environments and platforms, which ensures that all workflows and batch jobs can be managed consistently and efficiently.
  • Ease of Use
    The user interface is intuitive and user-friendly, making it easier for both technical and non-technical users to manage job workflows without extensive training.
  • Scalability
    Control-M scales effortlessly, accommodating the needs of small businesses to large enterprises, without compromising on performance.
  • Integrations
    It seamlessly integrates with numerous applications and technologies, including cloud services, databases, ERP systems, and more, which makes it versatile across different IT landscapes.
  • Advanced Automation Features
    Provides advanced automation capabilities such as predictive analytics, machine learning, and DR capabilities that enhance efficiency and reduce manual intervention.
  • Robust Reporting
    Offers powerful reporting tools and dashboards that provide actionable insights and visibility into job performance and system health.

Possible disadvantages of Control-M

  • Cost
    The comprehensive features and enterprise-level capabilities come at a high cost, which may be prohibitive for smaller organizations.
  • Complexity in Initial Setup
    The initial installation and configuration can be complex and require significant investment in time and resources to set up properly.
  • Learning Curve
    Despite its user-friendly interface, the depth and breadth of features can result in a steep learning curve for new users, necessitating substantial training.
  • Resource Intensive
    Control-M can be resource-intensive, requiring considerable computing resources to run efficiently, which might be a constraint for organizations with limited IT infrastructure.
  • Dependency on Vendor Support
    While support is robust, the complexity of the system can sometimes necessitate frequent interaction with vendor support, which can be time-consuming.
  • Customization Challenges
    While the tool is highly configurable, extensive customization can become complicated and may require professional services or advanced knowledge.

Apache Airflow features and specs

  • Scalability
    Apache Airflow can scale horizontally, allowing it to handle large volumes of tasks and workflows by distributing the workload across multiple worker nodes.
  • Extensibility
    It supports custom plugins and operators, making it highly customizable to fit various use cases. Users can define their own tasks, sensors, and hooks.
  • Visualization
    Airflow provides an intuitive web interface for monitoring and managing workflows. The interface allows users to visualize DAGs, track task statuses, and debug failures.
  • Flexibility
    Workflows are defined using Python code, which offers a high degree of flexibility and programmatic control over the tasks and their dependencies.
  • Integrations
    Airflow has built-in integrations with a wide range of tools and services such as AWS, Google Cloud, and Apache Hadoop, making it easier to connect to external systems.

Possible disadvantages of Apache Airflow

  • Complexity
    Setting up and configuring Apache Airflow can be complex, particularly for new users. It requires careful management of infrastructure components like databases and web servers.
  • Resource Intensive
    Airflow can be resource-heavy in terms of both memory and CPU usage, especially when dealing with a large number of tasks and DAGs.
  • Learning Curve
    The learning curve can be steep for users who are not familiar with Python or the underlying concepts of workflow management.
  • Limited Real-Time Processing
    Airflow is better suited for batch processing and scheduled tasks rather than real-time event-based processing.
  • Dependency Management
    Managing task dependencies in complex DAGs can become cumbersome and may lead to configuration errors if not properly handled.

Control-M videos

+ Add

Control-M Version 8 Overview

More videos:

  • Review - Control-M Self Service Overview
  • Review - Connect With Control-M: Control-M/Server 9 High Availability

Apache Airflow videos

+ Add

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Control-M and Apache Airflow)

Control-M

Apache Airflow

IT Automation
100 100%
IT Automation
0% 0
Workflow Automation
20 20%
Workflow Automation
80% 80
Monitoring Tools
100 100%
Monitoring Tools
0% 0
Automation
0 0%
Automation
100% 100

User comments

Share your experience with using Control-M and Apache Airflow. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Control-M and Apache Airflow

Control-M Reviews

Top 10 Control-M Alternatives in ’23
Job scheduling: On G2, the job scheduling feature receives the highest score with 9.4. However, Control-M alternatives, ActiveBatch and Redwood obtain higher scores for each category under functionality than Control-M (See Figure 5). Integrations/APIs: A user mentioned API and integration to other applications as a weak capability of the tool (Figure 1).
Source: research.aimultiple.com
9 Control-M Alternatives & Competitors In 2023
Verdict: Redwood platform offers better performance and visibility than the Control-M. This tool supports over 25 scripting languages and interfaces such as Python, R, and PowerShell with built-in syntax highlighting and parameter replacement. It also features advanced architecture and provides safe passage to businesses looking for Control-M alternatives through its...
Source: www.softwaretestingmaterial.com
The Top 5 BMC Control-M API Alternatives
Control-M Reports provide insights into job execution and performance. While the BMC Control-M interface provides robust reporting capabilities, there are also alternatives to generate reports using tools such as SQL and Hadoop. These tools can extract data from Control-M job logs and generate custom reports based on specific business requirements.
Source: www.redwood.com

Apache Airflow Reviews

5 Airflow Alternatives for Data Orchestration
While Apache Airflow continues to be a popular tool for data orchestration, the alternatives presented here offer a range of features and benefits that may better suit certain projects or team preferences. Whether you prioritize simplicity, code-centric design, or the integration of machine learning workflows, there is likely an alternative that meets your needs. By...
Source: www.kdnuggets.com
Top 8 Apache Airflow Alternatives in 2024
Apache Airflow is a workflow streamlining solution aiming at accelerating routine procedures. This article provides a detailed description of Apache Airflow as one of the most popular automation solutions. It also presents and compares alternatives to Airflow, their characteristic features, and recommended application areas. Based on that, each business could decide which...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped you explore the best Apache Airflow Alternatives available in the market. So, you can try hands-on on these Airflow Alternatives and select the best according to...
Source: hevodata.com
A List of The 16 Best ETL Tools And Why To Choose Them
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Apache Airflow programmatically creates, schedules and monitors workflows. It can also modify the scheduler to run the jobs as and when required.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, Apache Airflow seems to be more popular. It has been mentiond 71 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Control-M mentions (0)

We have not tracked any mentions of Control-M yet. Tracking of Control-M recommendations started around Mar 2021.

Apache Airflow mentions (71)

  • Data Engineering with DLT and REST
    This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / about 1 month ago
  • Data on Kubernetes: Part 3 - Managing Workflows with Job Schedulers and Batch-Oriented Workflow Orchestrators
    There are several tools available that can help manage these workflows. Apache Airflow is a platform designed to programmatically author, schedule, and monitor workflows. - Source: dev.to / 6 months ago
  • Apache Doris Job Scheduler for Task Automation
    Job scheduling is an important part of data management as it enables regular data updates and cleanups. In a data platform, it is often undertaken by workflow orchestration tools like Apache Airflow and Apache Dolphinscheduler. However, adding another component to the data architecture also means investing extra resources for management and maintenance. That's why Apache Doris 2.1.0 introduces a built-in Job... - Source: dev.to / 6 months ago
  • How I've implemented the Medallion architecture using Apache Spark and Apache Hdoop
    Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 7 months ago
  • 10 Open Source Tools for Building MLOps Pipelines
    An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 7 months ago
View more

What are some alternatives?

When comparing Control-M and Apache Airflow, you can also consider the following products

ManageEngine RecoveryManager Plus - RecoveryManager Plus is one such enterprise backup solution which has the ability to easily backup and restores both the domain controllers and virtual machines.

Make.com - Tool for workflow automation (Former Integromat)

Heroku Enterprise - Heroku Enterprise is a flexible IT management for developers that lets them build apps using their preferred languages and tools like Ruby, Java, Python and Node.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

SECDO - SECDO offers automated endpoint security and incident response solutions

Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.

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