What is a key characteristic of the Streaming batch aggregator?

• A. Requires streaming to start processing; memory heavy
• B. Stores all data in memory across jobs
• C. Receives all records in the job instance without running out of memory
• D. Only works with batch operations

Answer: (C)

The key idea is that the Streaming batch aggregator handles input in a streaming fashion within a batch process, so records flow through the job and are aggregated as they arrive rather than being loaded all at once. This means the system processes all records in the current job instance without requiring you to hold the entire dataset in memory, keeping memory usage bounded even with large inputs. In contrast, loading everything into memory across jobs or starting only when memory-heavy streaming would be needed isn’t how this pattern behaves. It’s designed to work with a streaming flow inside a batch context, not to rely on loading all data into memory or to be limited to non-streaming batch operations.