Stream Processing System Design Explained | Kafka, Flink, Spark, Exactly-Once, Window | FAANG Level
Stream processing is at the heart of modern real-time systems — powering fraud detection, real-time analytics, recommendations, monitoring, and AI pipelines at companies like Amazon, Netflix, Uber, and Google.
In this video, I walk through a complete stream processing system design using real-world architecture patterns, the same depth expected in FAANG system design interviews and senior backend roles.
📌 What You’ll Learn:
✅ Stream processing fundamentals (from first principles)
✅ How stream processing systems actually work internally
✅ Why batch systems fail for real-time use cases
✅ How Kafka, Flink, Spark Streaming differ
✅ How to reason about event time vs processing time
✅ Exactly-once vs at-least-once semantics
✅ Backpressure, watermarking, windowing, and state management
✅ Windowing strategies (tumbling, sliding, session windows)
✅ Designing scalable, fault-tolerant streaming pipelines
FEATURED PLAYLISTS
• System Design Patterns - • FAANG System Design Patterns
• System Design Tradeoffs - • FAANG System Design Tradeoffs
• Amazon Web Services (AWS) and Cloud Computing - • Amazon Web Service (AWS)
• System Design Concepts - • CAP Theorem & PACELC in Distributed System...
• Data Structures - • FAANG Data Structures
• Object Oriented Design Concepts - • FAANG Object Oriented Design Concepts
• Databases - • Databases
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