🚀 Uber Data Real-Time Streaming Pipeline

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📌 Overview

This project demonstrates a real-time data engineering pipeline for processing Uber trip data using modern big data technologies.

The pipeline ingests streaming data, processes it through multiple transformation layers (Bronze → Silver → Gold), and enables downstream analytics and insights.

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🏗️ Architecture

Data Source → Ingestion Layer → Bronze Layer → Silver Layer → Gold Layer → Analytics

🔹 Key Components:

• Ingestion Layer: Streaming data ingestion using Python
• Bronze Layer: Raw data storage (unprocessed)
• Silver Layer: Cleaned and transformed data
• Gold Layer: Aggregated, business-ready data
• Processing Engine: Apache Spark (Structured Streaming)

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⚙️ Tech Stack

• 🐍 Python
• ⚡ Apache Spark (Structured Streaming)
• ☁️ Azure Data Lake
• 📊 Databricks
• 🧠 SQL
• 📁 Delta Lake (optional enhancement)

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📂 Project Structure

Uber_Data_RealTimeStreaming/
│
├── Code_Files/
│   ├── ingest.py              # Data ingestion script
│   ├── bronze_adls.ipynb      # Bronze layer processing
│   ├── silver.py              # Silver transformations
│   ├── silver_obt.sql         # SQL transformations
│   ├── silver_obt.ipynb       # Notebook transformations
│   ├── model.py               # Data modeling / logic
│   └── readme.md              # Internal documentation
│
├── Data/                      # Sample or input data
├── templates/                 # Config/templates
├── .gitignore
├── .python-version
└── README.md

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🔄 Data Pipeline Flow

1️⃣ Ingestion (ingest.py)

• Reads real-time / batch data
• Pushes raw data into Bronze layer

2️⃣ Bronze Layer

• Stores raw, unprocessed data
• Schema is loosely enforced

3️⃣ Silver Layer (silver.py, silver_obt.sql)

• Data cleaning & transformation
• Handles nulls, duplicates, schema enforcement

4️⃣ Gold Layer (Modeling)

• Business-level aggregations
• Ready for dashboards & analytics

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▶️ How to Run

✅ Prerequisites

• Python 3.9+
• Apache Spark / Databricks
• Cloud Storage (Azure / AWS)

🔧 Steps

# Clone the repository
git clone https://github.com/namansinghal111/Uber_Data_RealTImeStreaming.git

# Navigate to project
cd Uber_Data_RealTimeStreaming

# Install dependencies (if applicable)
pip install -r requirements.txt

# Run ingestion
python Code_Files/ingest.py

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📊 Use Cases

• Real-time ride analytics 🚖
• Surge pricing insights 📈
• Driver & trip performance analysis
• Streaming ETL pipeline demo for interviews

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🌟 Features

• ✅ Real-time data processing
• ✅ Layered architecture (Bronze/Silver/Gold)
• ✅ Scalable & cloud-ready
• ✅ Modular code design
• ✅ Supports both batch & streaming

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🚧 Future Improvements

• Add Kafka for real-time ingestion
• Implement Delta Lake optimizations
• Add Airflow for orchestration
• Dashboard integration (Power BI / Tableau)
• CI/CD pipeline setup

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👨‍💻 Author

Naman Singhal

💼 Data Engineer | Cloud & AI Enthusiast ☁️ AWS | Azure | Databricks 🤖 AI/ML & Real-Time Systems

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⭐ Support

If you like this project:

• ⭐ Star the repo
• 🍴 Fork it
• 📢 Share with others

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📜 License

This project is licensed under the MIT License.