Section 1 - Instruction

Welcome to stream processing! Remember our batch processing lesson where we processed large chunks of data all at once? Stream processing is the opposite approach.

Instead of waiting for data to accumulate, stream processing handles data as it arrives - record by record, in real-time.

Engagement Message

What situations might require processing data immediately rather than waiting for batches?

Section 2 - Instruction

Think of batch processing like doing laundry - you collect dirty clothes all week, then wash everything at once. Stream processing is like a conveyor belt - items are processed continuously as they appear.

This continuous flow approach enables real-time responses to data events.

Engagement Message

Which one in the laundry vs conveyor belt analogy represents stream processing?

Section 3 - Instruction

Here's a practical example: imagine monitoring website clicks. Batch processing would collect all clicks for an hour, then analyze them together.

Stream processing analyzes each click as it happens, updating dashboards and triggering alerts in real-time.

Engagement Message

Which approach would be better for detecting a sudden spike in website traffic?

Section 4 - Instruction

Stream processing excels when you need immediate insights or responses. Think fraud detection - you want to block suspicious transactions instantly, not hours later.

Other examples include live chat systems, stock trading platforms, and IoT sensor monitoring.

Engagement Message

Why is immediate processing crucial for fraud detection scenarios?

Section 5 - Instruction

The key difference is latency - how long between data arrival and processing results. Batch processing has high latency (minutes to hours), while stream processing has low latency (seconds or less).

However, stream processing requires more complex infrastructure to handle continuous data flows.

Engagement Message

Why might continuous processing be more challenging than batch processing?

Section 6 - Instruction

Spark supports both batch and stream processing! Spark Streaming lets you apply similar transformations to continuous data streams as you would to static DataFrames.

The programming model stays familiar, but the execution handles real-time data ingestion and processing.

Engagement Message

What advantage does using the same framework for both batch and stream processing provide?

Section 7 - Practice

Type

Sort Into Boxes

Practice Question

Let's test your understanding of stream vs batch processing! Match each scenario with the better processing approach:

Labels

  • First Box Label: Batch Processing
  • Second Box Label: Stream Processing

First Box Items

  • Daily reports
  • Monthly billing
  • Weekly backups

Second Box Items

  • Fraud detection
  • Live chat
  • Stock alerts
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