https://github.com/gulomovazukhrakhon/kafka-stream-processing
💡 A real-time streaming pipeline for IoT sensor data using Kafka, Docker, and PostgreSQL. Fully containerized and simulates CSV-based sensor streaming with Python. Designed as a portfolio project.
https://github.com/gulomovazukhrakhon/kafka-stream-processing
data-pipeline docker iot kafka portfolio-project postgresql python real-time streaming
Last synced: 3 months ago
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💡 A real-time streaming pipeline for IoT sensor data using Kafka, Docker, and PostgreSQL. Fully containerized and simulates CSV-based sensor streaming with Python. Designed as a portfolio project.
- Host: GitHub
- URL: https://github.com/gulomovazukhrakhon/kafka-stream-processing
- Owner: gulomovazukhrakhon
- Created: 2025-06-26T07:03:22.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-06-26T07:09:20.000Z (3 months ago)
- Last Synced: 2025-06-26T08:23:05.014Z (3 months ago)
- Topics: data-pipeline, docker, iot, kafka, portfolio-project, postgresql, python, real-time, streaming
- Language: Python
- Homepage:
- Size: 23.2 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📡 Real-Time Environmental Sensor Data Pipeline
This project implements a real-time data pipeline using **Apache Kafka**, **Docker**, and **PostgreSQL** to simulate and process environmental sensor data from a CSV file. The pipeline mimics live IoT sensor streaming and stores the data for further analysis.
---
## 🚀 Project Overview
- 📁 Source: Kaggle Dataset – *Environmental Sensor Data (132k rows)*
- 🔄 Simulates real-time sensor data with `pandas`
- 🔌 Streams data into **Kafka topics** using a Python producer
- 📥 Kafka consumer reads the stream and stores it in **PostgreSQL**
- 🐳 Fully containerized with **Docker Compose**
- 📊 Kafka-UI for topic monitoring
---
## 🛠️ Technologies Used
| Component | Tech Stack |
|------------------|--------------------------------|
| Data Streaming | Apache Kafka |
| Messaging System | Kafka-Python (`kafka-python`) |
| Data Ingestion | Python + Pandas |
| Database | PostgreSQL |
| Orchestration | Docker + Docker Compose |
| Monitoring | Kafka-UI (ProvectusLabs) |
---
## 🗂️ Project Structure
```bash
elective2/
├── dataset/
│ └── iot_telemetry_data.csv
├── producer/
│ ├── producer.py
│ ├── requirements.txt
│ └── Dockerfile
├── consumer/
│ ├── consumer.py
│ ├── requirements.txt
│ └── Dockerfile
├── db/
│ └── init.sql
├── docker-compose.yml
└── README.md
```
---
## 🧠 Kafka Topic
- **Topic Name**: `kafka-topic-postgress`
- **Created Dynamically** if not already present
- Partitions: `1`
- Replication: `1`
---
## 🧪 How to Run This Project
### 1. Clone the Repository
```bash
git clone https://github.com/gulomovazukhrakhon/kafka-stream-processing.git
cd kafka-stream-processing
```
### 2. Run Docker Compose
```bash
docker-compose up --build
```
This will start:
* Kafka + Zookeeper
* PostgreSQL
* Producer and Consumer
* Kafka-UI on port `8080`
### 3. Access Kafka UI
📍 Open: http://localhost:8080
🧠 Topic: kafka-topic-postgress
---
## 🗄️ PostgreSQL Table: telemtry_data
Created automatically via init.sql. Structure:
| Column | Type |
|----------|------------------|
| ts | DOUBLE PRECISION |
| device | TEXT |
| co | DOUBLE PRECISION |
| humidity | DOUBLE PRECISION |
| light | BOOLEAN |
| lpg | DOUBLE PRECISION |
| motion | BOOLEAN |
| smoke | DOUBLE PRECISION |
| temp | DOUBLE PRECISION |
---
## 🔍 How It Works
1. producer.py reads CSV row by row
2. Sends each row as a JSON message to Kafka
3. Kafka holds messages under kafka-topic-postgress
4. consumer.py listens to the topic and inserts each message into PostgreSQL
5. Kafka UI displays messages in real-time
---
## ✅ Project Highlights
* ⏱️ Simulated real-time streaming (5s delay)
* 💾 Durable storage with PostgreSQL
* 🛡️ Error handling and retry logic for resilience
* 🔌 Modular and scalable architecture
* 🐳 Fully containerized and reproducible
---
## 📚 Dataset Source
[Kaggle: Environmental Sensor Data (132k rows)](https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k)
---
## 👩💻 Author
**Zukhrahon Gulomova**
Applied Artificial Intelligence, IU International University