List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Building a Real-Time Anomaly Detection System for IoT with Azure Event Hubs and Azure Stream Analytics

Time Series Analysis

Real-Time Anomaly Detection System for IoT with Azure Event Hubs and Azure Stream Analytics

  • Use Case:

    Detect anomalies in real-time sensor data from IoT devices such as temperature sensors, pressure gauges, or industrial equipment.

    Useful for predictive maintenance, fraud detection, and real-time alerts in sectors like manufacturing, energy, smart cities, and healthcare.

Objective

  • Build a low-latency, real-time data pipeline to ingest, process, and analyze IoT data streams.

    Apply anomaly detection logic (rules + ML models) on streaming data for early warning alerts.

    Ensure scalability, fault tolerance, and cost-efficiency with Azure’s cloud-native services.

Project Description

  • This project creates a real-time IoT anomaly detection pipeline using Azure’s event-driven and analytics services:

IoT Device Data Generation

  • IoT sensors/devices generate telemetry data (e.g., temperature, vibration, voltage).

Data Ingestion

  • Devices send continuous event streams to Azure Event Hubs, a scalable event ingestion service.

Real-Time Processing

  • Azure Stream Analytics subscribes to Event Hubs, processes events in real-time, and applies:

    Rules-based anomaly detection (e.g., thresholds, outliers).

    ML-based anomaly detection models using Azure Machine Learning integration.

Data Storage

  • Normal and anomalous data stored in Azure Data Lake Storage or Azure SQL Database for historical analysis.

Visualization & Alerts

  • Power BI dashboards visualize IoT data trends.
  • Azure Monitor / Logic Apps send real-time alerts (SMS, email, Teams) when anomalies occur.
  • Azure Services and Technologies :
    Component Azure Service / Technology
    IoT Device Connectivity Azure IoT Hub (optional) / Direct device → Event Hubs
    Event Streaming / Ingestion Azure Event Hubs
    Real-time Stream Processing Azure Stream Analytics (SQL-like queries, anomaly detection functions)
    Machine Learning Integration Azure Machine Learning (ML models embedded into Stream Analytics jobs)
    Storage for Processed Data Azure Data Lake Storage Gen2 / Azure SQL Database
    Visualization & Reporting Power BI