AWS Step Functions vs Airflow

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

AWS Step Functions

Stacks236

Followers391

Votes31

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.

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Advice on Airflow, AWS Step Functions

Prasad

Technology Specialist

Dec 25, 2019

Needs advice

create a task and manage the workflow- which is the best tool that can be used. I will list out the scenario • Create a ‘Task’ item and associate it with any type of underlying data (inspection, fuel check, etc). • Manage the state life cycle of an individual task. • Manage the Task through a workflow. • Suppress a task from user view • Manage access rights to a Task • Associate Items to a Task • Maintain the history / audit log of a task • Assign an owner/completion date • Escalation when tasks are not completed • Mark as ‘Read’

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Comments

Anonymous

Jan 19, 2020

Needs advice

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.

294k views294k

Comments

Detailed Comparison

Airflow	AWS Step Functions
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.	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.
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.	-
Statistics
Stacks 1.7K	Stacks 236
Followers 2.8K	Followers 391
Votes 128	Votes 31
Pros & Cons
Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Open source - provides minimum or no support 2 Running it on kubernetes cluster relatively complex 2 Observability is not great when the DAGs exceed 250 1 Logical separation of DAGs is not straight forward	Pros 7 Integration with other services 5 Easily Accessible via AWS Console 5 Complex workflows 5 Pricing 3 Scalability

What are some alternatives to Airflow, AWS Step Functions?

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

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.

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

PromptX

PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.

AI Autopilot

Agentic AI Platform for Intelligent IT Automation built by MSPs for MSPs. Revolutionize your operations with advanced AI agents.

iLeap

ILeap is a low-code app development platform to build custom apps and automate workflows visually, helping you speed up digital transformation.

Camunda

Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation.

Related Comparisons

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.

AWS Step Functions vs Airflow