3.1.4. Streaming Data Loading Patterns

💡 First Principle: Streaming data arrives continuously and must be processed with minimal latency—unlike batch processing where you can wait, collect, and process in bulk. The challenge is handling unbounded data, late arrivals, and out-of-order events while maintaining queryable historical data.

Scenario: IoT sensors send temperature readings every second. The analytics team needs near-real-time dashboards and historical analysis. The loading pattern must handle continuous ingestion while maintaining queryable historical data.

Streaming to Delta Lake Pattern

1. Ingest: Eventstream receives events from Event Hub
2. Process: Event processor filters and transforms
3. Write: Spark Structured Streaming writes to Delta table
4. Query: SQL or KQL queries Delta table

Visual: Streaming Load Pattern