Designing a 10M Events per Day Kafka to Spark Streaming Pipeline

🚀 Context

In one of my recent projects, I had to design a real-time data pipeline that could:

• Handle 10M+ events/day
• Deliver less than 5-second latency
• Guarantee exactly-once processing
• Maintain high reliability (99.9% uptime)

This post breaks down how I approached it.

⚠️ Problem

The existing system was batch-based:

• Around 8-minute delay in data availability
• Duplicate records due to retries
• No handling of late-arriving events
• Manual recovery on failures

👉 Result: unreliable analytics and delayed decision-making

🏗️ Architecture

Producers -> Kafka -> Spark Structured Streaming -> Delta Lake -> BI