Most data teams start by writing standalone Python scripts to pull from MySQL, Kafka, or REST APIs into a warehouse. That approach works when you have a handful of jobs. But as projects multiply, you end up with hundreds of scripts, each redoing connection setup, retry logic, logging, checkpointing, thread pools and error recovery. Copying and tweaking scripts becomes the norm, and you rebuild the same runtime infrastructure over and over. The pain point isn’t Python; it’s the fact that every sync job carries its own mini-framework instead of relying on shared code. Apache SeaTunnel tackles this by splitting pipeline definition from execution. You define three things—Source, Transform and Sink—and the SeaTunnel runtime takes care of connections, error handling, scaling and monitoring. Under the hood, a unified Connector Framework implements drivers for MySQL, Oracle, Kafka, S3, REST APIs and more. Every connector follows the same interface and lifecycle. That way, adding a new data source means picking or writing a connector, then wiring it into your pipeline, not reinventing retry policies or thread management in each project.