The streaming query will immediately fail if it falls behind the schedule.

The streaming job will drop any incoming records that it cannot process in real-time to keep up.

Spark will automatically add more executors (nodes) to handle the higher data volume in real-time.

The excess data will accumulate in memory or in the source buffer, creating a backlog and increased latency.