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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:
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.
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.
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.
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.
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.
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 - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of AWS Step Functions
- Integration with other services7
- Easily Accessible via AWS Console5
- Complex workflows5
- Pricing5
- Scalability3
- Workflow Processing3
- High Availability3
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of AWS Step Functions
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What is Airflow?
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