StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Cloud Task Management
  5. Airflow vs Amazon SWF

Airflow vs Amazon SWF

OverviewDecisionsComparisonAlternatives

Overview

Amazon SWF
Amazon SWF
Stacks35
Followers79
Votes0
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Amazon SWF: What are the differences?

Introduction

This Markdown code provides a comparison between Airflow and Amazon SWF, highlighting key differences between the two and organizing them in a specific format.

  1. Scalability: Airflow is highly scalable and can handle a large number of tasks and workflows concurrently, making it suitable for complex data processing. On the other hand, Amazon SWF is designed to scale automatically and can handle millions of concurrent tasks, making it perfect for highly scalable applications.

  2. Workflow Definition: Airflow allows you to define workflows and task dependencies using Python code, providing flexibility and extensibility. In contrast, Amazon SWF uses a domain-specific language (DSL) to define workflows, which can be more readable for non-technical stakeholders and provide a visual representation of the workflow.

  3. Managed Service: Airflow is a self-managed open-source tool, which means you need to set up and maintain the infrastructure yourself. Amazon SWF, on the other hand, is a fully managed service by AWS, providing automatic scaling, fault tolerance, and removing the need for infrastructure management.

  4. Integration with AWS Services: Amazon SWF seamlessly integrates with other AWS services such as Lambda, Step Functions, and Simple Queue Service (SQS), enabling you to build complex workflows within the AWS ecosystem. While Airflow has some third-party integrations for AWS services, it may require additional configuration and setup.

  5. Visibility and Monitoring: Airflow provides a user-friendly web interface that allows users to monitor and visualize the status of workflows, tasks, and dependencies. It also provides detailed logging and error handling. Amazon SWF offers a comprehensive console and API for monitoring and managing workflows, including features like real-time tracking and workflow metrics.

  6. Deployment Options: Airflow can be deployed in various environments such as on-premises, cloud, or containers, providing flexibility in choosing the deployment architecture. Amazon SWF is specifically designed to run on the AWS cloud infrastructure, limiting deployment options for applications hosted outside of AWS.

In Summary, Airflow and Amazon SWF differ in terms of scalability, workflow definition, managed service offering, integration with AWS services, visibility, monitoring capabilities, and deployment options.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Amazon SWF, Airflow

Anonymous
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

Amazon SWF
Amazon SWF
Airflow
Airflow

Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

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.

Maintaining application state;Tracking workflow executions and logging their progress;Holding and dispatching tasks;Controlling which tasks each of your application hosts will be assigned to execute
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
35
Stacks
1.7K
Followers
79
Followers
2.8K
Votes
0
Votes
128
Pros & Cons
No community feedback yet
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Amazon SWF, Airflow?

AWS Step Functions

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.

GitHub Actions

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

Apache Beam

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

Zenaton

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

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

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

Shipyard

na

PromptX

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.

iLeap

iLeap

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

AI Autopilot

AI Autopilot

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

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase