Day 1: Introduction to Real-Time Data Processing

Session 1: Fundamentals of Real-Time Data Analysis

• Overview of real-time data and streaming applications
• Key differences between batch and streaming data processing

Session 2: Introduction to Apache Kafka

• Kafka architecture and components
• Setting up a Kafka cluster and producing/consuming messages

Day 2: Streaming Data Processing with Spark

Session 1: Basics of Spark Streaming

• Overview of Spark Streaming and its integration with Kafka
• Setting up a Spark Streaming application

Session 2: Processing Streaming Data

• Transforming, filtering, and aggregating streaming data
• Practical demonstration: Processing data streams in real-time

Day 3: Advanced Techniques and Integration

Session 1: Advanced Spark Streaming Features

• Windowed operations and stateful processing
• Implementing windowed and stateful transformations

Session 2: Real-Time Data Pipeline Design

• Integrating Kafka, Spark Streaming, and external storage systems
• Case study: Building an end-to-end data pipeline

Day 4: Optimization and Real-World Applications

Session 1: Monitoring and Optimizing Data Pipelines

• Best practices for performance tuning and fault tolerance
• Optimizing a real-time data pipeline

Session 2: Applications and Deployment

• Case studies on real-time analytics in industries like finance, retail, and IoT
• Group activity: Designing and deploying a real-time analytics solution