What is the difference between batch processing and stream processing? When would you use each?

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Accepted Answer

Batch processing and stream processing are two fundamental approaches to handling data, each suited to different requirements.

Batch processing handles data in discrete chunks or batches at scheduled intervals. Data accumulates over a period, such as an hour or a day, and then the pipeline processes all of it at once. The processing runs on a schedule, like every night at midnight, and completes before the next batch begins. Traditional ETL workflows typically use batch processing.

Stream processing handles data continuously as it arrives, processing each event or small groups of events immediately. There is no waiting for data to accumulate. Events flow through the pipeline with minimal delay, often measured in seconds or milliseconds. Technologies like Apache Kafka, Apache Flink, and Amazon Kinesis enable stream processing.

Use batch processing when latency requirements are relaxed and data does not need to be available immediately. Batch is simpler to implement, easier to debug, and often more cost-effective because you can schedule jobs during off-peak hours. Reporting that updates daily, historical analysis, and overnight data warehouse loads are good batch use cases.

Use stream processing when you need real-time or near-real-time insights. Fraud detection must evaluate transactions as they happen, not hours later. Monitoring dashboards need current data to be useful. Recommendation systems should respond to user actions immediately. Streaming also works well when data arrives continuously and processing each event quickly makes more sense than storing it for later.

Many modern architectures combine both approaches. A streaming layer handles time-sensitive events in real-time, while a batch layer processes the same data more thoroughly for historical analysis. This hybrid approach, sometimes called the Lambda architecture, provides both immediacy and completeness.

The choice depends on business requirements. Ask how quickly stakeholders need data and whether real-time processing justifies the additional complexity and cost.