AWS Step Functions vs Apache Airflow comparison

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Executive Summary

We performed a comparison between Apache Airflow and AWS Step Functions based on real PeerSpot user reviews.

Find out in this report how the two Business Process Management (BPM) solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed AWS Step Functions vs. Apache Airflow Report (Updated: April 2024).
772,127 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
"Designing processes and workflows is easier, and it assists in coordinating all of the different processes.""Since Apache works very well on Python, we can manage everything and create pipelines there.""We're running it on a virtual server, which we can easily upgrade if needed.""The UI is very simple and easy to learn.""Apache Airflow is easy to use and can monitor task execution easily. For instance, when performing setup tasks, you can conveniently view the logs without delving into the job details.""Apache Airflow is in Python language, making it easy to use and learn.""The solution is quite configurable so it is easy to code within a configuration kind of environment.""The most valuable feature of Apache Airflow is creating and scheduling jobs. Additionally, the reattempt at failed jobs is useful."

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"What I like the most about Amazon Step Functions is how easy it is to use.""One can rate all the calls and that is a good feature.""It's Amazon, it's scalable.""AWS Step Functions acts as a high-level layer, allowing us to seamlessly integrate with microservices.""The integration capability is easy, whereas building state machines is tricky.""It's a general solution that you can adapt to your own needs and is simple to use. We like that it can be integrated with everything in the AWS suite, and that the creation of the pipeline can be done using the graphical user interface.""It is a scalable solution.""The solution is stable...The solution is easy to scale."

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"We have faced scenarios where Apache Airflow becomes non-responsive, leading to job failures. To resolve such situations, we had to manually reboot Apache Airflow since it doesn't provide an option to restart within the application. This necessitated modifying some configurations to initiate a restart of all Apache Airflow components. Although Apache Airflow is generally dependable, it may occasionally encounter glitches that can disrupt production flows and batches.""I have some issues with the solution's communication.""It would be beneficial to improve the pricing structure.""One specific feature that is missing from Airflow is that the steps of your workflow are not pipelined, meaning the stageless steps of any workflow. Not every workflow can be implemented within Airflow.""There is a need for more features on experimental evolution steps.""UI can be improved with additional user-friendly features for non-programmers and for fewer coding practitioner requirements.""The problem with Apache Airflow is that it is an open-source tool. You have to build it into a Kubernetes container, which is not easy to maintain, and I find it to be very clunky.""Technical support is an area that needs improvement."

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"It wasn't easy to understand the licensing model. It's like if you use just a little, it's cheap, but it becomes more expensive as you use more. It's like a hook that ties you inside the Amazon ecosystem. So, it creates a dependency.""The solution's data size limit can be improved.""The price and support are areas with shortcomings where the solution needs to improve.""The pricing of the solution can be improved.""Setup took about one day. We had some errors to understand in the beginning, but now everything is working good.""It is hard to coordinate the declaratory language.""The solution's pricing could be cheaper. It is cheaper than Airflow.""The interface can sometimes feel limited, as we're unable to see what AWS is running behind the scenes."

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Pricing and Cost Advice
  • "Apache Airflow is a free solution that can be downloaded and ready for use at any moment."
  • "The pricing for the product is reasonable."
  • "Although Airflow is open source software, there's also commercial support for it by Astronomer. We personally don't use the commercial support, but it's always an option if you don't mind the extra cost."
  • "We are using the open-source version of Apache Airflow."
  • "We use a community edition of Apache Airflow. It is open source and free."
  • "Apache Airflow is open-source and free of charge."
  • "It's open-source."
  • "The solution is open source so is free."
  • More Apache Airflow Pricing and Cost Advice →

  • "The solution's price is reasonable."
  • "The solution is expensive."
  • More AWS Step Functions Pricing and Cost Advice →

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    772,127 professionals have used our research since 2012.
    Questions from the Community
    Top Answer:Camunda Platform allows for visual demonstration and presentation of business process flows. The flexible Java-based option was a big win for us and allows for the integration of microservices very… more »
    Top Answer:Apache Airflow is easy to use and can monitor task execution easily. For instance, when performing setup tasks, you can conveniently view the logs without delving into the job details.
    Top Answer:The integration capability is easy, whereas building state machines is tricky.
    Top Answer:The major feature of AWS Step Functions is interdependency. Step Functions can determine what action to take next if one step returns a false status based on predefined logic. Step Functions aims to… more »
    Top Answer:For Step Functions error handling, one must use function calls and logging for error detection within state machines. Comparatively, AirFlow offers more room for improvement. It's like drag and drop… more »
    Average Words per Review
    Average Words per Review
    Also Known As
    Amazon Step Functions, Step Functions
    Learn More

    Apache Airflow is an open-source workflow management system (WMS) that is primarily used to programmatically author, orchestrate, schedule, and monitor data pipelines as well as workflows. The solution makes it possible for you to manage your data pipelines by authoring workflows as directed acyclic graphs (DAGs) of tasks. By using Apache Airflow, you can orchestrate data pipelines over object stores and data warehouses, run workflows that are not data-related, and can also create and manage scripted data pipelines as code (Python). 

    Apache Airflow Features

    Apache Airflow has many valuable key features. Some of the most useful ones include:

    • Smart sensor: In Apache Airflow, tasks are executed sequentially. The smart sensors are executed in bundles, and therefore consume fewer resources.
    • Dockerfile: By using Apache Airflow’s dockerfile feature, you can run your business’s Airflow code without having to document and automate the process of running Airflow on a server. 
    • Scalability: Because Apache Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers, you can easily scale it. 
    • Plug-and-play operators: With Apache Airflow, you can choose from several plug-and-play operators that are ready to execute your tasks on many third-party services.

    Apache Airflow Benefits

    There are many benefits to implementing Apache Airflow. Some of the biggest advantages the solution offers include:

    • User friendly: Using Apache Airflow requires minimal python knowledge to get started.
    • Intuitive user interface: The Apache Airflow user interface enables you to visualize pipelines running in production, monitor progress, and also troubleshoot issues when needed.
    • Easy integration: Apache Airflow can easily be integrated with cloud platforms (Google, AWS, Azure, etc).
    • Visual DAGs: Apache Airflow’s visual DAGs provide data lineage, which facilitates debugging of data flows and also aids in auditing and data governance. 
    • Flexibility: Apache Airflow provides you with several ways to make DAG objects more flexible. At runtime, a context variable is passed to each workflow execution, which is quickly incorporated into an SQL statement that includes the run ID, execution date, and last and next run times.
    • Multiple deployment options: With Apache Airflow, you have several options for deployment, including self-service, open source, or a managed service.
    • Several data source connections: Apache Airflow can connect to a variety of data sources, including APIs, databases, data warehouses, and more.  

    Reviews from Real Users

    Below are some reviews and helpful feedback written by PeerSpot users currently using the Apache Airflow solution.

    A Senior Solutions Architect/Software Architect says, “The product integrates well with other pipelines and solutions. The ease of building different processes is very valuable to us. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. It's very easy to create flows.”

    An Assistant Manager at a comms service provider mentions, “The best part of Airflow is its direct support for Python, especially because Python is so important for data science, engineering, and design. This makes the programmatic aspect of our work easy for us, and it means we can automate a lot.”

    A Senior Software Engineer at a pharma/biotech company comments that he likes Apache Airflow because it is “Feature rich, open-source, and good for building data pipelines.”

    AWS Step Functions lets you coordinate multiple AWS services into serverless workflows so you can build and update apps quickly. Using Step Functions, you can design and run workflows that stitch together services such as AWS Lambda and Amazon ECS into feature-rich applications. Workflows are made up of a series of steps, with the output of one step acting as input into the next. Application development is simpler and more intuitive using Step Functions, because it translates your workflow into a state machine diagram that is easy to understand, easy to explain to others, and easy to change. You can monitor each step of execution as it happens, which means you can identify and fix problems quickly. Step Functions automatically triggers and tracks each step, and retries when there are errors, so your application executes in order and as expected.

    Sample Customers
    Agari, WePay, Astronomer
    Alpha Apps, The Guardian, SGK, Bigfinite
    Top Industries
    Comms Service Provider20%
    Financial Services Firm20%
    Media Company15%
    Pharma/Biotech Company5%
    Financial Services Firm23%
    Computer Software Company14%
    Manufacturing Company7%
    Financial Services Firm29%
    Computer Software Company13%
    Manufacturing Company7%
    Insurance Company5%
    Company Size
    Small Business27%
    Midsize Enterprise6%
    Large Enterprise67%
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise73%
    Small Business67%
    Midsize Enterprise11%
    Large Enterprise22%
    Small Business14%
    Midsize Enterprise9%
    Large Enterprise77%
    Buyer's Guide
    AWS Step Functions vs. Apache Airflow
    April 2024
    Find out what your peers are saying about AWS Step Functions vs. Apache Airflow and other solutions. Updated: April 2024.
    772,127 professionals have used our research since 2012.

    Apache Airflow is ranked 2nd in Business Process Management (BPM) with 31 reviews while AWS Step Functions is ranked 12th in Business Process Management (BPM) with 9 reviews. Apache Airflow is rated 8.0, while AWS Step Functions is rated 7.8. The top reviewer of Apache Airflow writes "Enable seamless integration with various connectivity and integrated services, including BigQuery and Python operators ". On the other hand, the top reviewer of AWS Step Functions writes "Simplifies complex task automation and enhances development workflows while offering user-friendly interface, seamless scalability and efficient workflow orchestration". Apache Airflow is most compared with Camunda, Informatica Cloud API and App Integration, IBM BPM, IBM Business Automation Workflow and Bizagi, whereas AWS Step Functions is most compared with Camunda, IBM BPM, Pega BPM, Oracle BPM and IBM Business Automation Workflow. See our AWS Step Functions vs. Apache Airflow report.

    See our list of best Business Process Management (BPM) vendors.

    We monitor all Business Process Management (BPM) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.