1296  Stream Processing for IoT

Prerequisites: Big Data Overview | Data Storage

This enables: Anomaly Detection | Edge Compute Patterns

1296.1 Overview

Stream processing is essential infrastructure for modern IoT systems requiring real-time insights and actions. This comprehensive guide covers everything from fundamental concepts to production implementation patterns.

A modern autonomous vehicle generates approximately 1 gigabyte of sensor data every second from LIDAR, cameras, radar, GPS, and inertial measurement units. If you attempt to store this data to disk and then query it for analysis, you’ve already crashed into the obstacle ahead. The vehicle needs to make decisions in under 10 milliseconds based on continuously arriving sensor streams.

This is the fundamental challenge that stream processing solves: processing data in motion, not data at rest.

TipMinimum Viable Understanding: Streaming vs Batch Processing

Core Concept: Stream processing analyzes data continuously as it arrives (event-by-event), while batch processing accumulates data into chunks and processes them at scheduled intervals.

Why It Matters: The choice determines your system’s latency profile. A factory safety shutdown requiring <100ms response cannot wait for hourly batch jobs. Conversely, training ML models on streaming data adds unnecessary complexity when overnight batch processing suffices.

Key Takeaway: Ask “What is the cost of delayed insight?” If your anomaly alert loses value after 10 seconds, stream process it. If your trend analysis is equally useful whether computed at midnight or noon, batch process it and save on infrastructure complexity.

1296.2 Chapter Contents

This chapter is organized into the following sections:

1296.2.1 1. Stream Processing Fundamentals

Core concepts including batch vs stream processing, event time vs processing time, and windowing strategies (tumbling, sliding, session windows). Includes interactive pipeline demo and worked examples.

1296.2.2 2. Stream Processing Architectures

Deep dive into Apache Kafka + Kafka Streams, Apache Flink, and Spark Structured Streaming. Architecture comparison, performance benchmarks, and technology selection guidance.

1296.2.3 3. Building IoT Streaming Pipelines

Complete guide to designing and implementing production IoT streaming pipelines, from requirements through architecture to implementation stages.

1296.2.4 4. Handling Real-World Challenges

Late data and watermarks, exactly-once processing semantics, backpressure management, checkpointing, and fault tolerance strategies.

1296.2.5 5. Common Pitfalls and Worked Examples

Real-world pitfalls including non-idempotent processing and missing backpressure. Features comprehensive fraud detection pipeline worked example.

1296.2.6 6. Hands-On Lab: Basic Stream Processing

45-minute Wokwi lab implementing continuous streaming, windowed aggregations, event detection, and circular buffers on ESP32.

1296.2.7 7. Hands-On Lab: Advanced CEP

60-minute advanced lab covering pattern matching, complex event processing (CEP), session windows, and statistical aggregation.

1296.2.8 8. Interactive Game and Summary

Interactive Data Stream Challenge game with 3 difficulty levels testing windowing, CEP, and anomaly detection skills. Plus chapter summary and next steps.

1296.3 Quick Reference

Topic Key Concepts Go To
Windowing basics Tumbling, sliding, session windows Fundamentals
Technology selection Kafka vs Flink vs Spark Architectures
Pipeline design Ingestion, enrichment, aggregation Pipelines
Late data handling Watermarks, allowed lateness Challenges
Exactly-once semantics Idempotency, transactions Challenges
Fraud detection example Complete worked example Pitfalls
ESP32 streaming lab Hands-on embedded implementation Basic Lab
CEP patterns Pattern matching, sequences Advanced Lab

1296.4 What’s Next

Continue your journey in IoT data management: