Examples of Streaming Workloads

C

A streaming workload is a type of data processing that involves the continuous processing of data as it is generated or received, rather than batch processing. In other words, data is processed in real-time or near real-time as it arrives, rather than being stored and processed later.

Out of the given scenarios, option C - sending telemetry data from edge devices, is an example of a streaming workload. Telemetry data is generated by sensors and devices in real-time, and needs to be processed as soon as possible to extract valuable insights or take appropriate actions.

For example, consider a scenario where a company is using sensors to monitor the temperature and humidity levels in a warehouse. The sensors generate telemetry data every few seconds or minutes, and this data needs to be processed in real-time to ensure that the warehouse is maintained at the appropriate conditions. The data can be used to trigger alerts or notifications to maintenance staff, or to adjust the HVAC system to maintain the desired temperature and humidity levels.

Option A - sending transactions that are older than a month to an archive, is an example of a batch processing workload, where data is processed in large batches, rather than in real-time. Similarly, option D - sending cloud infrastructure metadata every 30 minutes, is also an example of a batch processing workload, where data is processed at fixed intervals.

Option B - sending transactions daily from point of sale (POS) devices, may or may not be a streaming workload, depending on the specific requirements of the use case. If the transactions need to be processed in real-time, such as to update inventory levels or trigger alerts for fraudulent transactions, then it would be a streaming workload. However, if the transactions are only processed once a day to update the accounting system or generate reports, then it would be a batch processing workload.