Count occurrences of each event type in real time, write the result to a BigQuery table, and handle connection and streaming errors.

Start by creating a Spark Structured Streaming job that reads from Kafka. Parse the JSON payload to extract the event_type field. Use groupBy("event_type").count() to get per‑event counts. Write the aggregated DataFrame to BigQuery using the BigQuery connector inside a foreachBatch function. Inside that function, wrap the write call in a try/except block; on exception, log the error and optionally retry a few times with a short delay. Set a checkpoint location so that Spark can resume from the last successful batch if the stream stops. This approach ensures real‑time counting, persistence to BigQuery, and graceful handling of transient errors.