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Airflow vs AWS Step Functions: What are the differences?

AWS Step Functions and Apache Airflow are both popular workflow management tools used in the field of data engineering and automation. Here are the key differences between AWS Step Functions and Apache Airflow:

  1. Architecture and Deployment: AWS Step Functions is a fully managed service provided by Amazon Web Services (AWS) that operates in the cloud. It follows a serverless architecture, where you don't have to worry about infrastructure management, scaling, or maintenance. On the other hand, Apache Airflow can be deployed on-premises, in the cloud, or in a hybrid environment, providing you with more deployment flexibility.

  2. Workflow Definition: AWS Step Functions uses a state machine-based approach to define and manage workflows. It provides a visual interface where you can design workflows using states and transitions, allowing for a graphical representation of the workflow structure. In contrast, Apache Airflow employs Directed Acyclic Graphs (DAGs) to define workflows. DAGs represent tasks and their dependencies in a code-based format, providing a more programmatic way of defining workflows.

  3. Integration with Services: AWS Step Functions seamlessly integrates with multiple AWS services, including Lambda, Batch, and ECS, enabling effortless incorporation of various AWS offerings into your workflows. On the other hand, Apache Airflow provides a broader range of integrations beyond AWS. It offers a rich library of operators and hooks, enabling connectivity with diverse services and platforms, both within and outside of the AWS environment.

  4. Monitoring and Logging: AWS Step Functions provides built-in monitoring and logging capabilities. It offers comprehensive tracking of workflow progress, capturing execution data, and allowing you to set up alarms for critical events. Apache Airflow also provides monitoring and logging features but may require more manual configuration and customization based on specific requirements.

In summary, AWS Step Functions is a fully managed, serverless service that offers a visual workflow designer and seamless integration with AWS services. It provides simplicity in deployment and is well-suited for those primarily operating within the AWS ecosystem. Apache Airflow, on the other hand, provides more deployment flexibility, a code-based workflow definition using DAGs, and a broader range of integrations beyond AWS. It is suitable for those looking for a more customizable solution that can adapt to various infrastructure and service requirements.

Advice on Airflow and AWS Step Functions
Needs advice
Apache SparkApache Spark

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 244.2K views

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval -

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Pros of Airflow
Pros of AWS Step Functions
  • 50
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
  • 6
    Open source
  • 5
    Complex workflows
  • 5
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
  • 7
    Integration with other services
  • 5
    Easily Accessible via AWS Console
  • 5
    Complex workflows
  • 5
  • 3
  • 3
    Workflow Processing
  • 3
    High Availability

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Cons of Airflow
Cons of AWS Step Functions
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
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    What is Airflow?

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

    What is AWS Step Functions?

    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.

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    What companies use Airflow?
    What companies use AWS Step Functions?
    See which teams inside your own company are using Airflow or AWS Step Functions.
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    What tools integrate with Airflow?
    What tools integrate with AWS Step Functions?

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    What are some alternatives to Airflow and AWS Step Functions?
    It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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