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Airflow vs Github Actions: What are the differences?
Key Differences between Airflow and Github Actions
Introduction
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:
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.
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.
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.
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.
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.
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.
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.
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 - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of GitHub Actions
- Integration with GitHub8
- Free5
- Easy to duplicate a workflow3
- Ready actions in Marketplace3
- Configs stored in .github2
- Docker Support2
- Read actions in Marketplace2
- Active Development Roadmap1
- Fast1
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of GitHub Actions
- Lacking [skip ci]5
- Lacking allow failure4
- Lacking job specific badges3
- No ssh login to servers2
- No Deployment Projects1
- No manual launch1