Data Analytics and Machine Learning on Big Data 📊🤖
Big Data analytics involves examining large datasets to uncover hidden patterns, correlations, and insights. Machine Learning (ML) enhances this by enabling predictive modeling.
Frameworks & Tools:
- Apache Spark MLlib: Scalable ML library
- TensorFlow, PyTorch: Deep learning frameworks adapted for large datasets
- Kafka & NiFi: For real-time data ingestion and preprocessing
Workflow Example:
- Data ingestion (Kafka or Spark Streaming)
- Data cleaning and feature extraction
- Model training using scalable ML libraries
- Model deployment for real-time predictions
Real-World Use Case: E-commerce platforms leveraging Big Data to recommend products based on browsing behavior and purchase history.
Diagram:
[Data Sources] --> [Ingestion & Streaming] --> [Data Processing] --> [Model Training] --> [Deployment & Insights]