A microservice is the better choice for the handler since it gives you modularity and easier scaling. Python usually works best for calling Spark because of strong PySpark support, while .NET Core can integrate via REST (like Livy) but with some overhead. For best practices, think containers, Kubernetes, and Airflow to keep it efficient and open source.

Interestingly, setting up the right flow here is a lot like how asset recovery services work—having the right tools and structure in place makes handling complex data smooth and reliable.