Intermediate2 days
Azure Stream Analytics
Real-time data processing and analytics on fast-moving streams
Overview
Azure Stream Analytics is a fully managed, serverless real-time analytics service for processing high-velocity data streams from IoT devices, applications, and event buses. Using a familiar SQL-like query language enriched with temporal windowing functions, you can detect anomalies, enrich streams with reference data, trigger alerts, and route results to any Azure destination — all without managing infrastructure. This training covers job authoring, windowing patterns, machine learning integration, IoT Edge deployment, and production monitoring.
What you'll learn
- Design and deploy Azure Stream Analytics jobs for real-time data processing
- Connect jobs to Event Hubs, IoT Hub, and Blob Storage inputs
- Write Stream Analytics queries using windowing functions (tumbling, hopping, sliding, session)
- Enrich streaming data with reference data joins from Azure SQL Database
- Route processed output to SQL Database, ADLS Gen2, Power BI, and Cosmos DB
- Monitor job health, diagnose errors, and scale jobs using streaming units
Programme
Day 1 — Job architecture, inputs & windowing
- Stream Analytics architecture: inputs, queries, outputs, and streaming units
- Connecting inputs: Event Hubs, IoT Hub, and Blob Storage
- Understanding event time vs processing time and handling late-arriving data
- Windowing functions: tumbling, hopping, sliding, and session windows explained
- Reference data joins: enriching live streams with static lookup tables
- Hands-on: build a real-time fraud detection job using windowed aggregations
Day 2 — Advanced patterns, outputs & production
- Outputs deep-dive: SQL, ADLS Gen2, Cosmos DB, Power BI streaming datasets, and Azure Functions
- Geospatial analytics: geofencing and location-based stream processing
- Anomaly detection using built-in ML functions (AnomalyDetection_SpikeAndDip)
- IoT Edge jobs: running Stream Analytics on the device edge for ultra-low latency
- CI/CD pipelines for Stream Analytics with VS Code and Azure DevOps
- Hands-on: build an end-to-end IoT telemetry pipeline with a real-time Power BI dashboard
Who is this for?
- Data engineers building real-time data pipelines on Azure
- IoT developers processing device telemetry streams
- Analytics engineers building real-time dashboards and alerting systems
- Teams adding streaming capabilities to existing batch analytics pipelines
Prerequisites
- Solid SQL knowledge — Stream Analytics uses an SQL-like query language
- Familiarity with Azure services (portal, resource groups)
- Basic understanding of event-driven architectures is helpful
Tools & technologies covered
Azure Stream AnalyticsAzure Event HubsAzure IoT HubPower BIAzure Data Lake Storage Gen2Azure SQL DatabaseVS CodeAzure DevOps
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