Airflow vs Couler
Overview
Detailed Comparison
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. | It aims to provide a unified interface for constructing and managing workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. |
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. | Unified interface and imperative programming style for defining workflows with automatic construction of directed acyclic graph (DAG);
Extensible to support various workflow engines;
Reusable steps for tasks such as distributed training of machine learning models;
Automatic workflow and resource optimizations under the hood |
Statistics | |
GitHub Stars - | GitHub Stars 943 |
GitHub Forks - | GitHub Forks 88 |
Stacks 1.7K | Stacks 0 |
Followers 2.8K | Followers 2 |
Votes 128 | Votes 0 |
Pros & Cons | |
Pros
Cons
| No community feedback yet |
Integrations | |
| No integrations available | |
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