Comment by hobbyist

I often read that spark avoids the costly synchronization required in mapreduce, since it uses DAG's. Can someone explain how is that achieved. If the application so demands that you can launch jobs together, that can be done even with hadoop/mapreduce. If one job requires the output of another, then the job has to wait for synchronization whether its mapreduce or DAG.