The choice between batch and stream processing comes down to how quickly an answer is needed.
Most data work is batch, because most decisions can wait hours or a day.
Streaming earns its extra complexity only when a delay has a real cost.
A batch job reads a bounded set of data, such as yesterday's files, processes it and finishes.
Batch jobs are easy to rerun after a fault, since the input is fixed.
A stream job runs continuously, reading an endless flow of events as they arrive.
Events often arrive late or out of order, so stream jobs group them by when they happened, not when they arrived.
Windows, such as every 5 minutes, turn an endless stream into totals that can be reported.
Watermarks set how long to wait for late events before a window is closed.
Duplicates are common in streams, so jobs must handle an event seen twice without counting it twice.
Event logs such as Apache Kafka carry the streams between systems.
Some engines process a stream as a quick series of tiny batches, an approach called micro-batching.
Running a stream for speed and a batch job for accuracy over the same data is a common pattern.
In practice, fraud checks, live dashboards and recommendations use streams, while finance reports use batches.
The usual mistake is building a stream for a report that someone reads once a morning.