Media Event Stream Analytics with Spark Structured Streaming

Added Jun 2025 3 design docs

Streaming platforms and digital publishers succeed or fail on engagement, and engagement lives in event data: plays, pauses, skips, clicks, and session activity flowing in continuously from apps and players. Product teams need those events aggregated into trustworthy metrics within minutes, not in tomorrow's batch report, which demands a genuinely streaming analytics architecture. The intern constructs a real-time analytics platform on Databricks for media interaction events. User events arrive via Kafka while supplementary files land through Autoloader, and Spark Structured Streaming processes both into Delta Lake tables organized in a Medallion Architecture. DLT pipelines handle the ETL flow with declarative transformations and quality expectations, and the intern implements streaming joins to enrich raw events with content metadata, one of the trickier and most valuable patterns in stream processing. Gold layer tables compute engagement metrics such as views per title, session durations, and completion rates, visualized through Databricks SQL dashboards that refresh as new events arrive. Completing the platform teaches the intern watermarking, streaming state, and join semantics on real event data, plus lakehouse modeling with Python and SQL, demonstrating they can deliver the near-real-time metrics that media businesses actually run on.

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