Which solution minimizes compute costs when propagating new records in a batch write process?

The best solution for minimizing compute costs in a batch write process is to perform a batch read on the reviews_raw table and conduct an insert-only merge. This approach allows for efficient handling of new records by only adding those that did not previously exist in the target table, thus avoiding unnecessary computation associated with overwriting existing data.

An insert-only merge operation is beneficial because it reduces the need for complex logic that would otherwise be required to identify and update existing records. Instead of rewriting the entire dataset, the process focuses only on appending new records, which is typically less resource-intensive and time-consuming. This can lead to significant savings on compute costs, particularly when dealing with large datasets where the number of new records may be relatively small compared to the total volume.

While other approaches, such as deleting old data before inserting new data or using a streaming process, can also manage incoming records, they may involve higher computational overhead or operational complexity. For instance, deleting old data often requires scans and may trigger additional write amplification, resulting in increased compute costs. Similarly, employing a streaming approach can add latency and increase complexity, which does not necessarily yield cost savings compared to a simplified batch process.

Aggregating data before writing can reduce volume, but this process may also introduce additional