When you design how to process data, one question defines the architecture: do you need the results now, or can you wait? It is the choice between batch and real-time (streaming) processing — and choosing wrong brings too much complexity or too much slowness.
Batch: processing in blocks, from time to time
In batch processing, data accumulates and is handled together, at set intervals — hourly, overnight, at day's end. It is simple, robust and cheap. Most reports and analysis live perfectly well with data refreshed once a day.
Streaming: processing as it arrives
In real-time processing, each piece of data is handled as soon as it happens, with latency of seconds or less. It lets you react instantly — but it is more complex to build, more expensive to operate and harder to keep reliable.
The deciding question: what does waiting cost?
The key is not "which is more modern", but "how much does the delay cost". If a decision can wait until tomorrow morning with no harm, batch is enough. If every second of delay has a cost — fraud happening, a system failing — then streaming is justified.
Typical cases for each
- Batch: management reports, billing, historical analysis, daily dashboard refresh.
- Streaming: fraud detection, equipment monitoring, operational alerts, live recommendations.
The mistake of choosing real time "just because"
Streaming sounds more advanced, and it is tempting to build it without real need. But you pay in complexity, cost and fragility. Most business cases live very well with batch — and starting simple lets you evolve to real time only where it truly pays off.
In practice
Before building streaming, ask each case: if this data were only ready in a few hours, would we lose anything? If the answer is no, batch is cheaper and more robust. Where, in your business, does waiting for data really cost money?
