@Marvin how does Prefect compare with Dagster?

Here's an overview of Prefect compared to Dagster: 1. Ease of Use: Prefect allows users to quickly deploy on top of existing Python code, which simplifies productionizing processes. This ease and flexibility can significantly reduce the time required to move from proof of concept (POC) to production. Dagster, like Prefect, also aims to provide strong orchestration capabilities but emphasizes type systems and data contract validation, which may add a layer of complexity for some users. 2. Dynamic Workflows: Prefect embraces dynamic, DAG-free workflows, which is increasingly important for modern data engineering tasks involving streaming data and complex dependencies. This approach contrasts with traditional DAG-based workflow managers, including Dagster, which uses directed acyclic graphs as its core abstraction. 3. Deployment: Prefect's hybrid model simplifies deployment by using a central server, avoiding the need for deploying a new server for each orchestration, which can speed up the overall deployment process. 4. Observability and Reliability: Prefect offers strong observability and enables reliable orchestration across complex pipelines. This ensures that failures are detected and handled more efficiently without needing to analyze each tool individually. 5. Developer Experience: Prefect focuses on providing an extraordinary developer experience by removing unnecessary complexities and making workflows transparent and easily observable. Dagster also prioritizes a good developer experience with features like software-defined assets and data quality checks, which can be advantageous depending on the use case. For detailed insights, you might find it helpful to check out this blog post and the FAQ section of Prefect's documentation. Keep in mind, no direct comparative issues were found in the GitHub repository search.