How do I set up continuous data compaction in Apache Iceberg to keep our query costs as low as possible on AWS Athena?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I set up continuous data compaction in Apache Iceberg to keep our query costs as low as possible on AWS Athena?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
AWS Athena Apache Iceberg continuous compaction AWS Glue table optimization compaction documentation 2026 Google ↗
Athena Iceberg OPTIMIZE REWRITE DATA USING BIN_PACK VACUUM documentation AWS Google ↗
- Enabling compaction optimizer · docs.aws.amazon.com
- Optimizing Iceberg tables · docs.aws.amazon.com
- Optimize Iceberg tables · docs.aws.amazon.com
- Catalog-level table optimizers · docs.aws.amazon.com
- OPTIMIZE · docs.aws.amazon.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
AWS Athena Apache Iceberg continuous compaction AWS Glue table optimization compaction documentation 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Athena Iceberg OPTIMIZE REWRITE DATA USING BIN_PACK VACUUM documentation AWS OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |