You need to complete the PySpark code for the Spark Structured Streaming pipelines.

The solution requires spark.readStream with format( ' cloudFiles ' ) for Auto Loader, paired with .writeStream using mergeSchema=true and a checkpointLocation.

Auto Loader ' s cloudFiles source incrementally processes new JSON files without rescanning the entire directory. The mergeSchema option handles schema drift — when sensors add new fields, the target Delta table schema expands automatically instead of throwing a parse error. This directly addresses Contoso ' s requirement to ' support schema drift. '

The checkpointLocation is what gives the pipeline its resilience. Databricks writes the stream ' s committed offset and schema state to that path. If the cluster restarts, the engine reads the checkpoint and picks up exactly where it left off — no events are reprocessed, satisfying ' exactly-once semantics ' and ' resume processing after failures without reprocessing the data. '

Without a checkpoint, the stream would restart from the beginning on every cluster bounce, which is precisely the problem Contoso is trying to eliminate.