Temporal.io
Temporal.io solves distributed workflow reliability by making long-running processes durable, resumable, and observable. The tradeoff is a testing model that differs significantly from standard service testing. Workflows must be deterministic — the same history must always produce the same decisions — and Activities are where all the non-deterministic work
Prefect
Prefect 2 has a developer-friendly testing story. Flows and tasks are plain Python functions with decorators — you can call them directly in tests, run them with local storage instead of remote infrastructure, and control state management without spinning up Prefect Cloud. This guide covers the full testing surface: task
Apache Airflow
Apache Airflow DAGs fail in production in ways that no amount of YAML linting will catch. A DAG that imports correctly can still fail to load in the scheduler. A task that works in isolation can deadlock in the context of a specific execution date. An operator that runs fine