@Marvin how does Prefect compare to Temporal?

I couldn't find any specific documentation or GitHub issues directly comparing Prefect and Temporal. However, here's a general overview to help you understand some key differences: * Orchestration vs. Workflow Platform: - Prefect focuses primarily on data workflow orchestration. It's designed to run and monitor data pipelines, with a strong emphasis on ease of use and deployment, especially for complex DAGs (Direct Acyclic Graphs). - Temporal is a more general-purpose workflow as a code platform. It's designed for a wide variety of use cases, not just data workflows, and provides robust features for state management, retries, and long-running processes. * State Management: - Prefect: Uses state management abstractions that allow for easy retries and failure handling within data workflows. - Temporal: Provides a more robust system for state management across complex workflows, supporting long-running activities and reliable execution across failures. * Ecosystem and Integrations: - Prefect has built-in integrations with popular data engineering tools and platforms, such as Dask, DBT, and various cloud services. - Temporal has broader integrations suitable for various applications, extending beyond data engineering. * Ease of Use: - Prefect is often praised for its user-friendly API, making it easier to define and orchestrate complex workflows using Python. - Temporal requires more investment in learning its model, but it offers strong guarantees and flexibility once mastered. If you need an in-depth comparison based on specific use cases or more detailed information, visiting each project's documentation or community forums might be beneficial: - Prefect Documentation: https://docs.prefect.io - Temporal Documentation: https://docs.temporal.io Feel free to refine your queries or ask about specific features, and I'll be happy to look up more detailed information.