Do you have experience with both batch and stream processing?

Yes, I have worked on both batch and stream processing systems. In batch processing, I used Spark SQL to process large historical datasets, applying transformations and aggregations in a single job. For stream processing, I implemented Spark Structured Streaming to ingest real‑time data from Kafka, applying windowed aggregations and writing results to a downstream database. The main difference is that batch processes entire datasets at once, while stream processing handles continuous data flows with low latency. Understanding the trade‑offs—latency vs. throughput, fault tolerance, and state management—is essential for choosing the right approach.