Software Alternatives, Accelerators & Startups

Pipedream VS Apache Airflow

Compare Pipedream VS Apache Airflow and see what are their differences

Pipedream logo Pipedream

Integration platform for developers

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Pipedream Landing page
    Landing page //
    2023-08-24
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Pipedream features and specs

  • No-Code Integration
    Pipedream allows users to connect services and automate workflows without needing extensive coding skills, making it accessible for non-developers.
  • Extensive Integrations
    Pipedream supports a wide range of APIs and services, enabling users to connect various platforms and tools seamlessly.
  • Scalability
    Pipedream can handle large volumes of data and complex workflows, which makes it suitable for both small and large-scale operations.
  • Real-Time Event Sourcing
    Pipedream allows real-time monitoring and processing of events, which is beneficial for applications needing instant updates.
  • Community Support
    The platform has a strong community of users and extensive documentation, providing plenty of resources and examples to help users get started.
  • Flexibility
    Users can write custom code when needed to ensure that integrations and workflows meet specific requirements.

Possible disadvantages of Pipedream

  • Pricing
    While Pipedream offers a free tier, advanced features and higher usage levels can become costly for freelance developers and small businesses.
  • Learning Curve
    Despite being a no-code platform, there can be a learning curve associated with understanding how to leverage all the features effectively.
  • Limited Offline Support
    Pipedream is a cloud-based service, and its functionality is limited when offline access is needed, which can be a drawback for some use cases.
  • Dependency on External Services
    As with any integration platform, workflow stability can be affected by the uptime and performance of third-party APIs and services used.
  • Privacy Concerns
    Handling sensitive data through an external platform can raise privacy and security concerns, especially in regulated industries.

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.

Pipedream videos

Using Event Sources and Workflows: Analyze Twitter Sentiment in Real-Time and Save to Google Sheets

More videos:

  • Demo - Managing the Concurrency and Execution Rate of Workflow Events
  • Demo - Save Zoom Cloud Recordings to Google Drive and Share on Slack

Apache Airflow videos

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Pipedream and Apache Airflow)
Automation
21 21%
79% 79
Workflow Automation
9 9%
91% 91
Web Service Automation
29 29%
71% 71
API Tools
100 100%
0% 0

User comments

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Reviews

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

Pipedream Reviews

Zapier: The $5B unbundling opportunity
Finally, Pipedream focuses on better support for complex Zapier use-cases by providing a platform that software engineers can use to create more technical and nuanced integrations.

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...
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.
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.

Social recommendations and mentions

Based on our record, Apache Airflow should be more popular than Pipedream. It has been mentiond 75 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.

Pipedream mentions (49)

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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
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What are some alternatives?

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

Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.

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

n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.

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

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.

Pipefy - Pipefy is a process management software that empowers anyone to create and automate efficient workflows on their own without code.