You run multiple scheduled Spark jobs on Cloud Dataproc; some jobs run sequentially and some concurrently. You need to automate and orchestrate these jobs. What should you use?

Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It's ideal for complex, scheduled workflows involving multiple steps and dependencies, like your Spark jobs that run sequentially and concurrently. A Directed Acyclic Graph (DAG) in Cloud Composer allows you to define these dependencies and execution order clearly. Cloud Dataproc Workflow Templates are good for defining a series of jobs that run on a single cluster, but they lack the advanced scheduling and dependency management of Cloud Composer for multiple, independent job flows. Initialization actions are for configuring clusters at startup, not for orchestrating ongoing job execution. A Bash script would be difficult to maintain, monitor, and scale compared to a managed orchestration service.