Batch vs Stream Processing
Analyze data processing paradigms: Bounded historical datasets (Spark/Hadoop) vs Unbounded real-time event streams (Flink/Kafka Streams), and the Lambda vs Kappa architecture evolution.
01.1. Bounded vs Unbounded Data Processing
All computational data pipelines operate on one of two fundamental data models:
- Batch Processing (Bounded Data): Data has a defined beginning and end (e.g., all transaction records for August 2026, or a 500GB CSV export).
- Execution: High-throughput MapReduce/Spark jobs scheduled periodically (hourly, nightly).
- Latency: Minutes to hours.
- Resource Utilization: Spiky (heavy CPU/RAM consumption during job runs, idle between runs).
- Stream Processing (Unbounded Data): Data is infinite and continuous, arriving record-by-record with no termination (e.g., credit card swipes, mobile GPS telemetry, live web clicks).
- Execution: Continuously running stateful daemons (Apache Flink, Kafka Streams).
- Latency: Milliseconds to sub-second.
- Resource Utilization: Smooth, continuous CPU/memory consumption.
Data Architecture Evolution: Lambda vs Kappa Architecture
Data Architecture Evolution: Lambda vs Kappa Architecture
Transitioning from dual batch-and-speed codebases to unified real-time stream processing.
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