Data Systems
Stream Processing
Stream processing enables real-time data analysis and event-driven architectures. Master the fundamentals of event time, windowing, state management, and exactly-once processing semantics.
- Real-Time â Process events as they arrive, not in batches
- Stateful â Maintain context across events for complex analytics
- Exactly-Once â Guarantee each event is processed exactly once
Stream processing turns data in motion into data at rest.
Stream Processing Fundamentals
Batch vs Stream Processing
| Aspect | Batch Processing | Stream Processing |
|---|---|---|
| Data | Bounded (finite) | Unbounded (infinite) |
| Latency | Minutes to hours | Milliseconds to seconds |
| Throughput | High (optimized for volume) | Moderate (optimized for latency) |
| Use Case | Analytics reports, ETL | Real-time dashboards, alerts |
| Complexity | Simpler | More complex (state, ordering) |
Event Time vs Processing Time
Windowing Strategies
| Window Type | Description | Use Case |
|---|---|---|
| Tumbling | Fixed-size, non-overlapping | Hourly aggregates |
| Sliding | Fixed-size, overlapping | Rolling averages |
| Session | Activity-based with gap | User session analysis |
| Global | All events in one window | Running totals |
Exactly-Once Semantics
| Guarantee | Description | Complexity |
|---|---|---|
| At-most-once | May lose events, no duplicates | Low |
| At-least-once | May have duplicates, no losses | Moderate |
| Exactly-once | No losses, no duplicates | High |
State Management
| State Backend | Use Case |
|---|---|
| RocksDB | Large state, local storage |
| Heap | Small state, fast access |
| External | Shared state, distributed |
Stream Processing Frameworks
| Framework | Model | State Management | Exactly-Once |
|---|---|---|---|
| Apache Flink | Event-time, true streaming | RocksDB, heap | Yes |
| Spark Streaming | Micro-batch | Checkpointing | Yes |
| Kafka Streams | Event-time, embedded | RocksDB | Yes |
Practice Exercises
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Windowing Design: Design a stream processing pipeline that computes the average temperature from IoT sensors every 5 minutes, with a 1-minute late tolerance. What windowing strategy would you use?
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Exactly-Once Design: Explain how you would achieve exactly-once semantics for a stream processing pipeline that reads from Kafka, transforms events, and writes to PostgreSQL.
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State Management: Design a fraud detection system that flags transactions more than 3 standard deviations above a user's average. What state needs to be maintained, and how do you handle state recovery?
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Architecture Comparison: Compare Flink and Spark Streaming for a real-time analytics dashboard. What are the trade-offs in terms of latency, complexity, and fault tolerance?
What to Learn Next
-> Kafka Deep Dive Event streaming, partitioning, and exactly-once semantics.
-> Batch Processing MapReduce, Spark, and distributed batch processing.
-> Event-Driven Architecture Event sourcing, CQRS, and message-driven systems.
-> Message Queues Async processing, event-driven architecture, and pub/sub patterns.
-> Data Lake Architecture Storage, processing, and governance for large-scale data.
-> Realtime Analytics Design Designing real-time analytics dashboards.