Why events arrive late, how Spark watermarks and Stream Analytics tolerances decide what counts, and six strategies to measure lateness, size watermarks and correct late data instead of losing it.
A hands-on guide to Spark Structured Streaming: the micro-batch model, sources and sinks, output modes, triggers, watermarks, foreachBatch upserts and monitoring, with output from a local run.
Step-by-step: stream Event Hubs events into bronze and silver Delta tables on Azure Databricks with the Kafka connector, a Unity Catalog service credential, watermarks and deduplication.
Ten mistakes that make Azure data platforms slow, insecure or unreliable, from tiny files and leaked keys to failure paths that report success, with the fix for each.
What belongs in the bronze, silver and gold layers of a lakehouse, with a worked PySpark and Delta example, a placement guide for common tasks, and the trade-offs behind how strictly to follow the pattern.