To build a scalable data engineering system that collects, processes, and analyzes IoT sensor data in real time. The platform aims to monitor environmental parameters, detect anomalies, and generate actionable insights using distributed data storage and processing frameworks.
Study IoT data formats and streaming protocols.
Simulate IoT sensor data (temperature, humidity, air quality).
Build data ingestion pipelines using MQTT or REST APIs.
Store incoming data in a distributed database like MongoDB or Cassandra.
Implement real-time processing using Apache Spark or Flink.
Develop anomaly detection rules based on threshold analysis.
Perform time-series data aggregation and window-based computations.
Design dashboards for monitoring real-time metrics.
Integrate cloud storage for scalable data backup.
Implement alerts for abnormal sensor readings.
Ensure system scalability for handling high-frequency data streams.
Optimize storage using compression techniques.
Secure IoT data transmission using encryption.
Conduct system performance benchmarking.
Document complete architecture including ingestion, processing, and visualization layers.
