Batch and stream processing are two fundamental data processing paradigms. Batch processes large amounts of data together (e.g., daily reports). Stream processes data continuously as it arrives (e.g., real-time metrics). Batch is simpler and more economical; stream is lower latency and more complex. Modern data teams often use both.
Batch Processing vs Stream Processing
Side-by-Side Comparison
| Aspect | Batch | Stream |
|---|---|---|
| Processing Model | Process data in chunks/batches. Collect data, run job, output results. Offline-first. | Process data continuously as it arrives. Real-time results. Always-on pipelines. |
| Latency | Hours to days between data arrival and results. Daily batch jobs at midnight common. | Milliseconds to seconds latency. Data analyzed as it arrives. True real-time. |
| Tools | Hadoop MapReduce, Apache Spark, Kubernetes batch jobs. Mature, stable, proven. | Apache Kafka, Apache Flink, Spark Streaming, AWS Kinesis. Younger but rapidly maturing. |
| Complexity | Simpler to design and debug. Run once, output generated. Error handling straightforward. | Complex state management across batches. Exactly-once guarantees hard. Stateful processing intricate. |
| Cost | Run jobs on-demand or scheduled. Pay for compute time. Cost-effective for large data. | Keep infrastructure running 24/7. Continuous costs. More expensive for small data volumes. |
| Use Cases | ETL pipelines, data warehousing, reports, machine learning model training, nightly aggregations. | Real-time fraud detection, IoT sensors, live dashboards, recommendation engines, log analysis. |
| Scalability | Scales to petabytes. Spark can process 100TB in hours. Horizontal scaling works well. | Scales to millions of events/sec. Message queues distribute load. Different scaling model. |
| Indian Tech Usage | Flipkart batch processes daily orders. PharmEasy nightly ETL for inventory. | Swiggy stream processes real-time delivery tracking. Paytm streams fraud detection signals. |
When to Use Each
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Verdict
Verdict: Start with batch processing (simpler, cheaper, proven). Add streaming when you need real-time insights or fraud detection. Most data teams use both: batch for analytics/warehousing, stream for real-time features. Modern platforms like Apache Spark Streaming blur the line. Understanding both paradigms is essential for modern data engineering.