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Hi, Tom from Temporal here. I don't have a lot of experience with Apache Airflow personally, but I was at Cloudera when it was added to our Data Engineering service, so I learned about it at the time. Here are a few things that come to mind: * Both Apache Airflow and Temporal are open source * Both create workflows from code, but the approach is different. With Airflow, you write some code and then generate a DAG that Airflow can execute. With Temporal, your code is your workflow, which means you can use your standard tools for testing, debugging, and managing your code. * With Airflow, you must write Python code. Temporal has SDKs for several languages, including Go, Java, TypeScript, and PHP. The Python SDK is already in beta and there's work underway for a .NET SDK. * Airflow is pretty focused on the data pipeline use case, while Temporal is a more general solution for making code run reliably in an unreliable world. You can certainly run data pipeline workloads on Temporal, but those are a small fraction of what developers are doing with Temporal (more here: https://temporal.io/use-cases). |