AWS Step Functions

Build distributed applications using visual workflows

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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.


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BENIFITS

Build and update apps quickly
AWS Step Functions lets you build visual workflows that enable fast translation of business requirements into technical requirements. You can build applications in a matter of minutes, and when needs change, you can swap or reorganize components without customizing any code.
Improve resiliency
AWS Step Functions manages state, checkpoints and restarts for you to make sure that your application executes in order and as expected. Built-in try/catch, retry and rollback capabilities deal with errors and exceptions automatically.
Write less code
AWS Step Functions manages the logic of your application for you, and implements basic primitives such as branching, parallel execution, and timeouts. This removes extra code that may be repeated in your microservices and functions.
Data processing
Step Functions can help ensure that long-running, multiple ETL jobs execute in order and complete successfully, instead of manually orchestrating those jobs or maintaining a separate application. You can also use Step Functions to standardize a machine learning training workflow to improve the accuracy of machine learning models.

Use cases

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