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  5. Airflow vs StackStorm

Airflow vs StackStorm

OverviewDecisionsComparisonAlternatives

Overview

StackStorm
StackStorm
Stacks80
Followers186
Votes31
GitHub Stars6.4K
Forks774
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs StackStorm: What are the differences?

Key differences between Airflow and StackStorm

Airflow and StackStorm are both popular workflow automation tools, but they have distinct differences that set them apart.

  1. Architecture: Airflow follows a directed acyclic graph (DAG) model, where each task is represented as a node and dependencies between tasks are represented as edges. StackStorm, on the other hand, follows a rule-based approach, where rules are defined and triggered by events.

  2. Language support: Airflow supports Python natively, allowing users to write their workflows using Python code. StackStorm, on the other hand, supports multiple languages including Python, JavaScript, and Ruby, giving users more flexibility in choosing the language they are comfortable with.

  3. Community and ecosystem: Airflow has a larger and more mature community compared to StackStorm. This means that Airflow has a wider range of plugins, integrations, and community support available. StackStorm, although growing, has a smaller community and a more limited ecosystem of integrations and plugins.

  4. Workflow visualization: Airflow provides a web-based user interface that allows users to visualize their workflows as DAGs and track the progress of tasks. StackStorm, on the other hand, does not provide a built-in visualization tool for workflows, making it less intuitive to track the progress and dependencies of tasks.

  5. Event-driven vs time-based scheduling: Airflow primarily uses time-based scheduling, where tasks are scheduled to run at specific times or intervals. StackStorm, on the other hand, focuses on event-driven automation, where workflows are triggered by events or conditions. This makes StackStorm more suitable for real-time and event-driven workflows.

  6. Extensibility: Airflow allows users to extend its functionality by creating custom operators and hooks using Python. StackStorm also allows for extensibility through the use of custom sensors, actions, and rules. However, StackStorm's rule-based approach provides a more flexible and easier way to extend its functionality compared to Airflow's Python-centric approach.

In summary, Airflow and StackStorm have different architectural models, language support, community and ecosystem, workflow visualization capabilities, scheduling approaches, and extensibility options. Understanding these key differences can help organizations choose the right workflow automation tool for their specific needs.

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Advice on StackStorm, 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.

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Comments

Detailed Comparison

StackStorm
StackStorm
Airflow
Airflow

StackStorm is a platform for integration and automation across services and tools. It ties together your existing infrastructure and application environment so you can more easily automate that environment -- with a particular focus on taking actions in response to events.

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.

Automations tie events to actions you’d like to take, using a rules engine and, if you want, comprehensive workflow. Automations are your operational patterns summarized as code.;StackStorm automations work either by starting with your existing scripts – just add simple meta data – or by authoring the automations within StackStorm.;Automations are the heart of StackStorm – they allow you to share operational patterns, boost productivity, and automate away the routine.;CLI, REST API + Python Bindings
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
GitHub Stars
6.4K
GitHub Stars
-
GitHub Forks
774
GitHub Forks
-
Stacks
80
Stacks
1.7K
Followers
186
Followers
2.8K
Votes
31
Votes
128
Pros & Cons
Pros
  • 7
    Auto-remediation
  • 5
    Integrations
  • 4
    Complex workflows
  • 4
    Automation
  • 3
    Open source
Cons
  • 3
    Complexity
  • 1
    There are not enough sources of information
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
Integrations
Mailgun
Mailgun
VMware vSphere
VMware vSphere
Rackspace Cloud Servers
Rackspace Cloud Servers
Vault
Vault
Octopus Deploy
Octopus Deploy
Ansible
Ansible
Duo
Duo
PhantomJS
PhantomJS
Yammer
Yammer
Cassandra
Cassandra
No integrations available

What are some alternatives to StackStorm, Airflow?

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

Gunnery

Gunnery

If your application is divided into multiple servers, you are probably connecting to them via ssh and executing over and over the same commands. Clearing caches, restarting services, backups, checking health. Wouldn't it be cool if you could do that from browser or smartphone? Gunnery is here for you!

Flumio

Flumio

Flumio is a modern automation platform that lets you build powerful workflows with a simple drag-and-drop interface. It combines the power of custom development with the speed of a no-code/low-code tool. Developers can still embed custom logic directly into workflows.

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.

Vison AI

Vison AI

Hire AI Employees that deliver Human-Quality work. Automate repetitive tasks, scale effortlessly, and focus on business growth without increasing head count.

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