Data Analytics and Machine Learning on Big Data 📊🤖

Intermediate

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:

  1. Data ingestion (Kafka or Spark Streaming)
  2. Data cleaning and feature extraction
  3. Model training using scalable ML libraries
  4. 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]