Step Snap 1: [Batch vs Stream Processing]

Let me use vivid examples to explain the concepts of batch and stream processing through real-world analogies with Spark and Kafka.

Real-World Examples and Comparisons

Spark Batch Processing Scenario

Imagine a taxi company analyzing its daily revenue:

# Spark batch processing example
spark.read.csv("taxi_trips_jan15.csv")
    .groupBy("date")
    .agg(sum("revenue").alias("daily_revenue"))
    .write.saveAsTable("daily_revenue_report")

Think of this like a restaurant doing its daily accounting:

• Collects all receipts at the end of the day
• Processes all transactions in one go
• Calculates total revenue, tips, and popular dishes
• Generates comprehensive daily reports

Kafka Stream Processing Scenario

Real-time taxi location and status monitoring:

// Kafka stream processing example
KStream<String, TaxiEvent> taxiStream = builder
    .stream("taxi-events")
    .filter((key, taxi) -> taxi.getStatus().equals("active"))
    .mapValues(taxi -> updateLocation(taxi));

This is like a live GPS tracking system:

• Continuously receives location updates from each taxi
• Instantly processes and updates positions
• Perfect for real-time monitoring and quick responses

Application Scenarios

Batch Processing Use Cases