Incremental aggregation involves updating aggregate values incrementally as new data arrives or existing data is modified as per defined timeline, providing efficient and timely insights into evolving datasets without the need for re-computation from scratch. Incremental aggregation is more efficient compared to full aggregation, especially for large datasets, because it avoids the need to reprocess the entire dataset each time new data arrives. It reduces computational overhead and resource usage, making it suitable for real-time or near-real-time data processing scenarios.