Data processing systems fundamentally grapple with when data is complete enough for computation. Batch processing addresses this by waiting for a natural boundary, such as a completed file or a defined end time, before processing the entire dataset at once. This approach prioritizes data completeness over immediate results.

Streaming processing, conversely, aims for speed by producing answers from data as it arrives. This requires systems to estimate data completeness and manage scenarios where these estimates are incorrect. The core trade-off in streaming lies between data completeness and processing latency.

Batch processing strategies include full and incremental loads, along with large-window aggregation, with micro-batching offering a middle ground. Streaming encompasses techniques like tumbling, sliding, and session windows, watermarks for handling late data, and architectures like Lambda and Kappa. Understanding exactly-once processing is also critical in this domain.