IoT Data Pipeline & Real-Time Analytics

Overview

IoT devices generate continuous streams of telemetry data. Turning that data into value requires an architecture that can ingest it reliably, process it at the right speed, and route it to the right consumers — dashboards, alerting systems, ML models, or long-term storage. We design and implement end-to-end IoT data pipelines using Azure Stream Analytics, AWS Kinesis, and cloud-native data services. We select between stream and batch processing based on your use case: real-time anomaly detection demands sub-second responses, while operational reporting can leverage scheduled batch jobs for deeper analysis. We also implement orchestration with tools like Apache Airflow to coordinate complex multi-source data workflows.

• Stream processing with Azure Stream Analytics and AWS Kinesis
• Batch pipeline design for reporting and historical analysis
• Multi-source data integration (IoT, backend, external APIs)
• Horizontal and vertical scaling strategies for data growth
• Pipeline orchestration and DAG-based workflow management
• Real-time alerting and threshold-based notification systems