End to End architecture and Implementation of a Data ingestion Pipeline in AWS Cloud for building Data lake with MQTT based IOT Telemetry data coming from Cisco IOT gateways

Real-time Smart City Data Pipeline | Python · Apache Kafka · Apache Spark · AWS S3 · Docker A scalable IoT data streaming platform processing vehicle telemetry, traffic, weather, and emergency data using modern data engineering technologies.

nd-to-End Real-Time IoT Telemetry Pipeline. Streams high-frequency hardware metrics from an edge device to AWS using Kinesis Data Streams. Implements a Lambda Architecture with a Hot Path (DynamoDB) for sub-second monitoring and a Cold Path (S3) for historical Big Data archiving. Fully automated via Terraform (IaC) and visualized with Streamlit.

Real-time ingestion, storage, and analytics for simulated vehicle sensor data using fully managed AWS services.

Serverless IoT telemetry pipeline on AWS — end-to-end with Terraform, GitHub Actions, and CloudWatch

ETL pipeline that splits product IoT telemetry readings into daily parquet files (localstack or AWS)

🚗 Real-time Smart City IoT data pipeline with Kafka, Spark & AWS - Vehicle telemetry streaming analytics

End-to-end IoT telemetry pipeline: MQTT publisher/broker → PostgreSQL queue → FastAPI → AWS Lambda → S3. Fully containerized with Docker Compose.

This project demonstrates an IoT data pipeline using AWS IoT Core, Amazon Timestream, and Amazon Managed Grafana to collect, store, and visualize real-time telemetry data from a simulated IoT device. It enables scalable and secure data management with interactive dashboards for insights.

An end-to-end IoT data ingestion and processing pipeline supporting MQTT, CoAP, LoRaWAN, and Zigbee protocols. Captures telemetry from 12,000+ edge sensors, processes events via AWS Kinesis and Lambda, and stores time-series data in InfluxDB and S3.

The City Vehicle Data Pipeline is designed to process and migrate JSON data from IoT devices (such as vehicle telemetry data) stored in an AWS S3 bucket to an Azure SQL Database. This data pipeline ensures smooth data ingestion, validation, transformation, and loading into the Azure environment for further analysis and reporting.

A complete end-to-end IoT data pipeline that simulates an ESP32 device with motor control and safety interlocks sending real-time telemetry through MQTT, processes it with AWS Lambda, stores it in DynamoDB, and visualizes everything on a live Streamlit dashboard. Built entirely on LocalStack for local AWS development. Personal Project.

End-to-end IIoT pipeline using AWS IoT Core, S3, and SageMaker to predict machine failures in real-time. Features a Java simulator for telemetry, a Random Forest model for anomaly detection, and a Python inference engine for sub-second alerting. Built to demonstrate cloud-native MLOps and industrial AI.