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Apache ActiveMQ VS Apache Airflow

Compare Apache ActiveMQ VS Apache Airflow and see what are their differences

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Apache ActiveMQ logo Apache ActiveMQ

Apache ActiveMQ is an open source messaging and integration patterns server.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Apache ActiveMQ Landing page
    Landing page //
    2021-10-01
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Apache ActiveMQ features and specs

  • Open Source
    ActiveMQ is open-source under the Apache License, making it free to use and modify. This can lead to cost savings compared to commercial solutions.
  • Wide Protocol Support
    ActiveMQ supports multiple messaging protocols, including AMQP, MQTT, OpenWire, Stomp, and others, allowing for flexible integration with various systems and applications.
  • Java Integration
    Written in Java, ActiveMQ integrates well with JVM-based applications and other Apache projects like Camel and Karaf, making it a good fit for Java-centric environments.
  • High Availability
    Features like broker clustering, network of brokers, and failover support provide robust high availability options, ensuring message delivery even in case of failures.
  • Performance and Scalability
    ActiveMQ can handle a large number of messages and users by scaling horizontally, making it suitable for both small and enterprise-level applications.
  • Admin Console
    ActiveMQ provides a web-based admin console for easy management and monitoring of the message broker, simplifying administrative tasks.

Possible disadvantages of Apache ActiveMQ

  • Complex Configuration
    The initial setup and configuration can be complex, especially for newcomers. It often requires a steep learning curve to understand all the available options and optimizations.
  • Resource Intensive
    ActiveMQ can be resource-intensive, particularly in high-throughput scenarios, which may necessitate more robust hardware for optimal performance.
  • Latency
    In certain configurations, ActiveMQ may exhibit higher latency compared to other brokers, which might not make it suitable for use cases requiring real-time guarantees.
  • Java Dependency
    As a Java-based solution, ActiveMQ requires the JVM, which can be a downside for organizations that have standardized on other technology stacks.
  • Community Support
    While there is a community around ActiveMQ, it may not be as large or as active as those for other, similar open-source projects. This can lead to slower responses to issues and fewer community-based resources.
  • Documentation
    Though comprehensive, the documentation can sometimes be difficult to navigate, making it challenging for users to find specific information quickly.

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.

Apache ActiveMQ videos

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Apache Airflow videos

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Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Apache ActiveMQ and Apache Airflow)

Apache ActiveMQ

Apache Airflow

Data Integration
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Data Integration
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Workflow Automation
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Workflow Automation
100% 100
Stream Processing
100 100%
Stream Processing
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Automation
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Automation
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Reviews

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

Apache ActiveMQ Reviews

6 Best Kafka Alternatives: 2022’s Must-know List
ActiveMQ is a flexible, open-source, multi-protocol messaging broker that supports many protocols. This makes it easy for developers to use a variety of languages and platforms. The AMQP protocol facilitates integration with many applications based on different platforms. However, ActiveMQ’s high-end data accessibility capabilities are complemented by its load balancing,...
Source: hevodata.com
Top 15 Alternatives to RabbitMQ In 2021
It is a managed information broker for Apache ActiveMQ which has simple installation and it runs message broker in cloud. It doesn’t need any special look after regular management and maintenance of the message system. It is utilized to send bulk message services.
Source: gokicker.com
Top 15 Kafka Alternatives Popular In 2021
Apache ActiveMQ is a popular, open-source, flexible multi-protocol messaging broker. Since it has great support for industry-based protocols, developers get access to languages and platforms. It helps in connecting clients written in languages like Python, C, C++, JavaScript, etc. With the help of the AMQP protocol, integration with many applications with different platforms...
Source: www.spec-india.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 a lot more popular than Apache ActiveMQ. While we know about 75 links to Apache Airflow, we've tracked only 7 mentions of Apache ActiveMQ. 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.

Apache ActiveMQ mentions (7)

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Apache Airflow mentions (75)

  • The DOJ Still Wants Google to Sell Off Chrome
    Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 2 months ago
  • 10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
    Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 3 months ago
  • Data Orchestration Tool Analysis: Airflow, Dagster, Flyte
    Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / 4 months ago
  • AIOps, DevOps, MLOps, LLMOps – What’s the Difference?
    Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 4 months ago
  • 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 / 5 months ago
View more

What are some alternatives?

When comparing Apache ActiveMQ and Apache Airflow, you can also consider the following products

RabbitMQ - RabbitMQ is an open source message broker software.

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

IBM MQ - IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.

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

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

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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