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Bucketing not address the problem of having a large number of partitions in the metadata, which is the root cause of the query planning bottleneck. Converting to a columnar format like Apache Parquet will not directly reduce the overhead associated with managing a large number of partitions. Combining small objects will not mitigate the planning overhead that comes from a large number of partitions in the data catalog. Hence A and C
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
Creating an AWS Glue partition index and enabling partition filtering can significantly improve query performance when dealing with large datasets with many partitions. The partition index allows Athena to quickly identify the relevant partitions for a query, reducing the time spent scanning unnecessary data. Partition filtering further optimizes the query by only scanning the partitions that match the filter conditions. Athena partition projection based on the S3 bucket prefix is another effective technique to improve query performance. By leveraging the bucket prefix structure, Athena can prune partitions that are not relevant to the query, reducing the amount of data that needs to be scanned and processed. This approach is particularly useful when the data is organized in a hierarchical structure within the S3 bucket.