Batch vs Streaming
Data doesn't always need to be processed the moment it arrives. Sometimes waiting and processing in bulk makes more sense. Understanding when to use batch versus streaming processing is a fundamental skill in data engineering.
Batch Processing
Batch processing collects data over time, then processes it all at once on a schedule. Think of it like doing laundry — you wait until you have enough clothes, then wash everything together.
Collect data → Wait for schedule → Process all at once → Results available later
Common batch processing scenarios include daily sales reports, monthly billing calculations, and historical trend analysis. Tools like Apache Spark, dbt, and scheduled SQL jobs excel at batch workloads.
The advantages are significant: batch processing is simpler to build, more efficient for large volumes, and easier to debug when things go wrong. The tradeoff is delayed results — you won't see today's data until tomorrow's batch runs.
Stream Processing
Stream processing handles data continuously as it arrives. Each event is processed immediately, like a factory assembly line that never stops.
Data arrives → Process immediately → Results available now
Streaming shines when timing matters: fraud detection that must block suspicious transactions instantly, live dashboards showing current system status, or recommendation engines that adapt to user behavior in real-time. Tools like Apache Kafka, Apache Flink, and AWS Kinesis power streaming architectures.
Choosing the Right Approach
The decision often comes down to latency requirements and complexity tolerance.
| Factor | Batch | Streaming |
|---|---|---|
| Latency | Minutes to hours | Milliseconds to seconds |
| Complexity | Lower | Higher |
| Error handling | Easier (rerun the batch) | Harder (data keeps flowing) |
| Resource efficiency | High (process in bulk) | Variable (always running) |
| Use case | Reports, analytics | Real-time reactions |
Many systems use both approaches together. A streaming system might detect fraud in real-time while a batch system generates daily compliance reports from the same data. This hybrid approach — sometimes called the Lambda architecture — gives you the best of both worlds.
Start with batch processing unless you have a clear real-time requirement. It's easier to build, test, and maintain. Add streaming only when the business genuinely needs immediate results.