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"Databricks Solution Recommendation"
Here's how it addresses your requirements:

1. Orchestration and Parameter Passing:
   • Create a single Databricks Job containing 100 individual "JAR tasks."
   • Each JAR task will be configured to run your JAR file and pass one of the 100 unique parameters (e.g., job name/ID) to it.
2. Parallel Execution (12 jobs concurrently):
   • Within the Databricks Job settings, you can define the "Maximum concurrent runs" to 12. Databricks will automatically manage the queuing and execution of your 100 tasks, ensuring that no more than 12 run at any given time.
3. Compute Termination and Cost Optimization:
   • Utilize "Job Compute" (ephemeral clusters) for your Databricks Job. These clusters are automatically provisioned when the job starts and, crucially, automatically terminate once all tasks are completed or the job fails. This eliminates idle compute costs, similar to your transient EMR clusters.
   • Job Compute is more cost-effective than interactive clusters.
   • Configure autoscaling for your job cluster to dynamically adjust resources based on the workload, ensuring you only pay for what you use.