Need advice about which tool to choose?Ask the StackShare community!


+ 1
GitHub Actions

+ 1
Add tool

Airflow vs Github Actions: What are the differences?

Key Differences between Airflow and Github Actions


Airflow is a platform to programmatically author, schedule, and monitor workflows, while Github Actions is a platform for automating workflows in a Github repository. The key differences between Airflow and Github Actions are as follows:

  1. Workflow Orchestration: Airflow is specifically designed for workflow orchestration, providing a rich set of tools and features to create, schedule, and manage complex workflows. On the other hand, Github Actions focuses more on automating smaller, task-based workflows within the context of a Github repository.

  2. Flexibility and Extensibility: Airflow offers a high degree of flexibility and extensibility, allowing users to define their own custom operators and hooks to integrate with various systems. This gives Airflow the ability to handle a wide range of use cases and integrate with different technologies. In contrast, Github Actions provides a limited set of pre-defined actions and lacks the same level of flexibility to customize and extend the platform.

  3. Integration with External Systems: Airflow provides built-in integrations with various external systems and services, such as databases, cloud platforms, and messaging systems. It offers operators and hooks that can easily connect to these systems and perform actions. Github Actions, on the other hand, has a more limited range of integrations and requires additional setup and configuration to connect to external systems.

  4. Community and Ecosystem: Airflow has a large and active community, with a wide range of plugins, extensions, and resources available. This allows users to leverage existing solutions and knowledge when using Airflow. Github Actions, being a newer platform, has a smaller community and ecosystem, with fewer resources and integrations available compared to Airflow.

  5. Scalability and Performance: Airflow is designed to handle large-scale workflows and high-throughput processing, with features like task parallelism, distributed execution, and scalability. It can efficiently handle complex dependencies and run workflows across multiple machines or clusters. Github Actions, on the other hand, is more suited for smaller-scale workflows and lacks the same level of scalability and performance as Airflow.

  6. Pricing and Deployment: Airflow is typically deployed on dedicated infrastructure, either on-premises or in the cloud, and requires setup and maintenance of the infrastructure. It may incur additional costs for infrastructure and scaling. Github Actions, being a cloud-native platform, is hosted by Github and is available as part of Github's pricing plans. It provides a simpler setup and deployment process, without the need for managing infrastructure.

In summary, Airflow is a powerful workflow orchestration platform with extensive capabilities and flexibility, designed for complex and large-scale workflows. Github Actions, on the other hand, is a more lightweight and task-based automation platform, tightly integrated with Github repositories, but with more limited flexibility and scalability.

Advice on Airflow and GitHub Actions
Needs advice
Apache SparkApache Spark

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.

See more
Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 259.1K views

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 -

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Airflow
Pros of GitHub Actions
  • 51
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
  • 6
    Open source
  • 5
    Complex workflows
  • 5
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
  • 8
    Integration with GitHub
  • 5
  • 3
    Easy to duplicate a workflow
  • 3
    Ready actions in Marketplace
  • 2
    Configs stored in .github
  • 2
    Docker Support
  • 2
    Read actions in Marketplace
  • 1
    Active Development Roadmap
  • 1

Sign up to add or upvote prosMake informed product decisions

Cons of Airflow
Cons of GitHub Actions
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
  • 5
    Lacking [skip ci]
  • 4
    Lacking allow failure
  • 3
    Lacking job specific badges
  • 2
    No ssh login to servers
  • 1
    No Deployment Projects
  • 1
    No manual launch

Sign up to add or upvote consMake informed product decisions

What is Airflow?

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.

What is 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.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention Airflow and GitHub Actions as a desired skillset
What companies use Airflow?
What companies use GitHub Actions?
See which teams inside your own company are using Airflow or GitHub Actions.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Airflow?
What tools integrate with GitHub Actions?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

What are some alternatives to Airflow and GitHub Actions?
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.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
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.
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
See all alternatives