spark-structured-streaming

To build real-time data pipelines with Spark Structured Streaming, connect a Kafka source, define a streaming read, and write the continuous data into a Delta Lake table using a configured trigger interval like 30 seconds.

Implementing stateful operations like stream-stream joins and windowed aggregations in Spark Streaming requires defining watermarks to manage state and ensure accurate event-time processing across unbounded data streams.

Optimize Spark Structured Streaming performance for production workloads by applying best practices for checkpointing, configuring watermarks to drop late data, and tuning trigger intervals to balance latency and throughput.

Yes, you can process real-time clickstream data in Spark Streaming by ingesting events from Kafka, enriching them with user dimension data, and aggregating session activity into a Delta table for immediate dashboarding.

Watermarking and checkpointing are needed in Spark Structured Streaming pipelines when handling stateful operations to prevent unbounded state growth and ensure fault recovery by tracking processed data across streaming batches.