Real-Time Weather Data Processing and Prediction System

Project Overview

This project implements a comprehensive real-time weather data processing and machine learning prediction system using modern big data technologies. The system follows a medallion architecture (Bronze-Silver-Gold) to process streaming weather data from APIs, store it efficiently using Delta Lake, and provide real-time weather predictions using machine learning models.

Key Features

• Real-time Data Ingestion: Streams weather data from Weather API using Kafka
• Medallion Architecture: Implements Bronze-Silver-Gold data lake pattern with Delta Lake
• Machine Learning Pipeline: Trains and deploys ML models for weather prediction
• Scalable Processing: Uses Apache Spark for distributed data processing
• Cloud-Ready: Supports both local deployment and cloud platforms (Databricks)
• Containerized Services: Docker Compose setup for easy deployment
• Flexible Deployment: Local Apache Kafka or managed Aiven Kafka for cloud

Technology Stack

• Big Data Processing: Apache Spark, PySpark
• Storage: Delta Lake (ACID transactions, schema evolution)
• Streaming: Apache Kafka (local) / Aiven Kafka (cloud managed service)
• Machine Learning: Spark MLlib, Random Forest, Classification models
• Infrastructure: Docker, Docker Compose
• Cloud Platforms: Databricks, Aiven (managed Kafka)
• Data Sources: WeatherAPI.com
• Languages: Python, SQL

System Architecture

The system follows a modern data lakehouse architecture:

1. Data Ingestion Layer

• Kafka Producer (weather_producer.py): Fetches real-time weather data from WeatherAPI
• Streaming Ingestion: Kafka consumers process data streams in real-time
• Multiple Sources: Supports both API streaming and batch CSV processing

2. Storage Layer (Delta Lake)

• Bronze Layer: Raw data ingestion from multiple sources
  • from_api/: Real-time API data
  • from_csv/: Batch CSV data
• Silver Layer: Cleaned and merged data with business logic
• Gold Layer: Aggregated features and ML-ready datasets

3. Processing Layer

• Data Preprocessing: Feature engineering, data cleaning, labeling
• ML Training: Multiple model training for different weather predictions
• Real-time Inference: Streaming prediction pipeline

4. ML Models

The system trains specialized models for:

• Temperature prediction (3-hour ahead)
• Humidity forecasting
• Wind speed and direction prediction
• Weather classification
• Precipitation forecasting
• Atmospheric pressure prediction

Project Structure

code/
├── data-processing/           # Data preprocessing and feature engineering
├── kafka-streaming-data-ingest/  # Real-time data ingestion pipeline
├── quan-ly-tang-luu-tru/     # Delta Lake storage management
├── streaming-predict/         # Real-time prediction services
└── train-ml/                 # Machine learning model training

data/
└── user/delta/           # Delta Lake storage
    ├── bronze/               # Raw data layer
    ├── silver/               # Processed data layer
    ├── gold/                 # Analytics-ready data
    └── models/               # Trained ML models

Key Components

1. Kafka Streaming Pipeline

• Producer: Continuously fetches weather data from external APIs
• Consumer: Processes streaming data into Bronze layer
• Error Handling: Robust error handling and data validation

2. Data Processing Pipeline

• Bronze to Silver: Data cleaning, validation, and standardization
• Silver to Gold: Feature engineering and ML preparation
• Schema Evolution: Automatic schema management with Delta Lake

3. Machine Learning Pipeline

• Feature Engineering: Creates lag features, weather indicators
• Model Training: Multiple specialized prediction models
• Model Deployment: Real-time inference capabilities
• Model Versioning: Automated model management

4. Infrastructure

• Docker Compose: Easy local development setup with Apache Kafka
• Cloud Integration: Alternative deployment with Aiven managed Kafka service
• Monitoring: Stream processing monitoring and alerts

Technical Achievements

• Scalability: Handles high-velocity weather data streams
• Reliability: ACID transactions with Delta Lake ensure data consistency
• Performance: Optimized Spark configurations for efficient processing
• Flexibility: Supports multiple deployment environments
• Monitoring: Comprehensive logging and stream monitoring

Data Pipeline Flow

1. Ingestion: Weather data streams from API → Kafka
2. Bronze: Raw data stored in Delta format with schema validation
3. Silver: Data cleaning, deduplication, and business rule application
4. Gold: Feature-rich datasets optimized for ML and analytics
5. ML Training: Automated model training on processed features
6. Prediction: Real-time weather prediction serving

Skills Demonstrated

• Big Data Engineering: Apache Spark, Delta Lake, Kafka
• Machine Learning: MLlib, predictive modeling, feature engineering
• Data Architecture: Medallion architecture, data lake design
• DevOps: Docker containerization, CI/CD practices
• Cloud Computing: Multi-cloud deployment strategies
• Real-time Processing: Stream processing, real-time analytics
• Data Quality: Schema management, data validation, monitoring

Business Value

• Real-time Insights: Immediate weather data processing and predictions
• Cost Efficient: Open-source stack reduces operational costs
• Scalable: Handles growing data volumes without performance degradation
• Reliable: Enterprise-grade data consistency and fault tolerance
• Extensible: Modular design allows easy feature additions

Installation & Setup

1. Prerequisites: Docker, Docker Compose, Python 3.8+
2. Kafka Setup: docker-compose up -d in kafka-streaming-data-ingest/
3. Spark Environment: Configure Spark with Delta Lake extensions
4. API Keys: Set up WeatherAPI.com credentials
5. Data Pipeline: Run notebooks in sequence for data processing
6. ML Training: Execute training notebooks for model development

This project demonstrates expertise in modern data engineering, real-time processing, machine learning, and cloud-native architectures, making it suitable for roles in data engineering, ML engineering, and big data analytics.