DEA-C01 · Question #301
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches. Users…
The correct answer is D. Apply compaction. Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time
Question
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches. Users report slow performance for real-time tables. A data engineer reviews the real-time tables and notices that the tables are made up of many small data files The data engineer must improve the performance of the real-time tables. Which solution will meet this requirement?
Options
- AExpire historic snapshots.
- BArchive historic snapshots.
- CDelete S3 objects that are not linked from the Iceberg table.
- DApply compaction.
How the community answered
(38 responses)- A3% (1)
- B16% (6)
- C5% (2)
- D76% (29)
Explanation
Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time
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