Stream Analytics

June 12, 2026

Featured image: Handling late-arriving events, four numbered steps: measure lateness, size the watermark, pick the output mode, and correct what is dropped with a MERGE

Handling Late-Arriving Events in Real-Time Data Pipelines

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.

June 2, 2026

Featured image: Real-time data architectures on Azure, with Azure Event Hubs at the centre of an orbit of Databricks, Cosmos DB, Eventhouse, Stream Analytics, IoT Hub and Event Grid icons

Designing Real-Time Data Processing Architectures on Azure

How to design a real-time data platform on Azure: latency tiers, Event Hubs ingestion, choosing Stream Analytics, Databricks, Fabric or Functions, and the decisions on event time, partitions and replay.