Batch processing versus stream processing are two different approaches to data processing in Kapacitor tasks. Batch processing involves grouping data points into a specific time interval, querying InfluxDB periodically, and using limited memory. This approach is suitable for cases where aggregate functions need to be performed on the data, downsampling is required, or low latency is not a critical factor. On the other hand, stream processing creates subscriptions to InfluxDB, allowing every data point written to InfluxDB to also be written to Kapacitor, and is ideal for real-time transformations, cases where lowest possible latency is paramount, and high volume query loads on InfluxDB. The choice between batch and stream processing depends on the specific requirements of the task, including memory availability, latency needs, and the type of data being processed.