Can you explain some use cases you have recently handled in PySpark on Databricks?

Recently I worked on three key use cases in PySpark on Databricks. First, a real‑time log aggregation pipeline that ingested Kafka streams, performed windowed aggregations, and stored results in Delta Lake for low‑latency dashboards. Second, a batch ETL job that processed terabytes of clickstream data, applied feature engineering for a recommendation engine, and wrote the output to a Snowflake warehouse. Third, a data quality framework that scanned datasets for nulls, outliers, and schema drift, generating alerts via Databricks Jobs and sending notifications to Slack. Each use case required careful resource tuning, error handling, and integration with CI/CD for reproducible deployments.