DEA-C01 · Question #271
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format. The company is…
The correct answer is B. Use AWS Glue Data Catalog to automatically compact the Iceberg tables. D. Use AWS Glue Data Catalog to enable copy-on-write for the Iceberg tables. Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads. Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.
Question
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format. The company is experiencing slow query performance. Which solutions will improve query performance? (Choose two.)
Options
- AUse AWS Glue Data Catalog to generate column-level statistics for the Iceberg tables on a
- BUse AWS Glue Data Catalog to automatically compact the Iceberg tables.
- CUse AWS Glue Data Catalog to automatically optimize indexes for the Iceberg tables.
- DUse AWS Glue Data Catalog to enable copy-on-write for the Iceberg tables.
- EUse AWS Glue Data Catalog to generate views for the Iceberg tables.
How the community answered
(49 responses)- A4% (2)
- B78% (38)
- C12% (6)
- E6% (3)
Explanation
Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads. Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.
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