As soon as enough data is available, an automatic trigger creates a cluster and starts data processing automatically. After processing, the cluster is shut down again, resulting in high cost savings. Even if the solution with Spark would be possible as a 24/7 streaming job, the "On-Demand Batch Job" variant is preferred, since it is a cluster of about 2TB RAM, which is thus only switched on 2-3 hours a day.