Welcome to Day 25 of the Spark Mastery Series. Today we move from “reading streams” to real-time analytics. This is where most streaming pipelines fail - not because of code, but because of state mismanagement.

Let’s fix that.

🌟 Why Streaming Aggregations Are Hard

Streaming data never ends. If you aggregate without limits, Spark keeps data forever.

Result:

• Growing state
• Memory pressure
• Job crashes

🌟 Event Time Is Mandatory Always use event time, not processing time.

Why?

• Processing time depends on delays
• Event time reflects real business time Correct analytics depend on event time.

🌟 Windows - Turning Infinite into Finite

Windows slice infinite streams into manageable chunks.

Example:

• Sales every 10 minutes
• Clicks per hour
• Orders per day

🌟 Watermarking — Cleaning Old State

Watermark tells Spark: ‘You can forget data older than X minutes.’

This:

• Bounds memory usage
• Allows append mode
• Handles late data safely

🌟 Real-World Example E-commerce

• Window: 5 minutes
• Watermark: 10 minutes

Meaning:

• Accept data late by 10 minutes
• Drop anything older This balances accuracy and performance.

🚀 Summary

We learned:

• Streaming aggregations
• Event time vs processing time
• Windowed analytics
• Tumbling & sliding windows
• Late data handling
• Watermarking
• Output modes

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