title: What are the best use cases for aws glue python shell jobs vs. spark jobs?
body: <p>Have been using aws glue python shell jobs to build simple data etl jobs, for spark job, only have used once or twice for converting to orc format or executing spark sql on JDBC data. So wondering which are the best/typical use cases for each of them?
  Some document says python shell job is suitable for simple jobs whereas spark for more complicated jobs, is that correct? Could you please share more experience on this?</p>
  
  <p>Many thanks</p>