Stream processing large number of JSON files and handling exception

[VijayP]

1. application writes several JSON  (small)  files and the expected volumes of these files are high ( Estimate: 1 million during the peak season in a hourly window) . As per current design, these files are streamed through Spark Stream and we use autoloader to load these files. 
1. Can Databricks bronze Job handle the volume without any failures loading to Bronze table. ?
2. Reading several forms appears streaming a large parquet file is not ideal .
2. Handling Stream Processing failures through quarantine process
1. Any exception occurring we would like to write those records for operation’s review. Are there any best practices / reference material with example for handling the same?

[cgrant]

We have customers that read millions of files per hour+ using Databricks Auto Loader. For high-volume use cases, we recommend enabling file notification mode, which, instead of continuously performing list operations on the filesystem, uses cloud native offerings to "push" new file locations to Auto Loader.

For handling bad records, we have the rescuedDataColumn and for JSON files in particular, columnNameOfCorruptRecord. You can use these in tandem with some additional filtering logic to quarantine bad records away.