The suggested answer is A.
Here's a breakdown of why this is the most cost-effective solution and why the others are less suitable:

A. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
- This approach is efficient because it only processes the data necessary to update the deduplicated table. The insert-only merge operation avoids costly full table scans or overwrites. By using a composite key consisting of user_id, review_id, product_id, and review_timestamp, it ensures proper deduplication even if reviews arrive out of order.
B. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
- While Structured Streaming with "trigger once" can simulate a batch job, it introduces the overhead of the streaming engine. For a nightly batch process, the benefits of streaming (low latency, continuous processing) are not realized, making it less cost-effective than a simple batch read and merge. Also, it does not inherently address the deduplication requirement described in the question.
- Reason for not choosing: This method introduces unnecessary overhead compared to a standard batch read and merge operation.
C. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
- Delta Lake's version history is useful, but in this scenario, it relies on the assumption that only new data is appended to the reviews_raw table. The problem statement mentions that "some records may be delayed as they await moderator approval." If a delayed record arrives, simply taking the difference between versions would miss this record. This option is not robust against late-arriving data.
- Reason for not choosing: This solution does not correctly handle potentially delayed records.
D. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
- Filtering by `review_timestamp` and appending records from the last 48 hours is problematic. The question says records are stored in a YYYY/MM/DD structure and the ingestion job appends data for the *previous* date. Therefore, filtering on the last 48 hours does not align with the daily ingestion. Moreover, it does not guarantee deduplication and could lead to duplicate records in the target table if the job is run more frequently than daily, or if there is any overlap in timestamps due to timezones or data ingestion issues.
- Reason for not choosing: The time filtering logic is flawed given the daily data ingestion pattern and the method does not address deduplication of records.
E. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
- Reprocessing and overwriting the entire table is the least efficient option. It consumes maximum compute resources since it reprocesses existing, already deduplicated data. This approach becomes increasingly expensive as the data volume grows.
- Reason for not choosing: This approach is highly inefficient and costly, as it reprocesses all data, including data that has already been processed.

Therefore, option A provides the most optimized solution with minimal cost. It only processes new records and handles the deduplication using a natural composite key.

The AI recommends sticking with the suggested answer A.
Reason: Suggested answer A is an efficient and reliable way to deduplicate and propagate records at minimal compute cost.

Citations:

Delta Lake Documentation, https://docs.delta.io/latest/