If AWS Glue Data Catalog’s Iceberg optimizers do not fit your lakehouse, the main alternatives are to run Apache Iceberg maintenance procedures on compute you operate, or to use a managed platform for tables it owns. These options are not interchangeable: compare who owns the table and which operations are supported, especially orphan-file cleanup.
What does AWS Glue’s Iceberg optimizer do?
AWS Glue Data Catalog offers three separate maintenance functions for Apache Iceberg tables. They address different problems, so replacing one does not necessarily replace the others.
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| Function | What it does | Glue options or effect |
|---|---|---|
| Compaction | Rewrites fragmented small data files to improve table layout. | Binpack, sort, or Z-order strategies. |
| Snapshot retention | Expires older snapshots according to configured retention requirements. | Reduces available time-travel and rollback history as snapshots expire. |
| Orphan-file deletion | Deletes data or metadata files no longer referenced by table metadata. | Removes unreferenced files, but needs a safe retention interval so active writes are not mistaken for orphans. |
AWS documents configuring these optimizers for individual Iceberg tables through the console, CLI, or API. AWS announced Glue Data Catalog Iceberg table storage optimization in September 2024; that is the launch date, not a guarantee about present-day feature scope or regional availability. Check current AWS documentation for your deployment.
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Which alternatives are available?
| Option | Table ownership and maintenance | Key limitation or trade-off |
|---|---|---|
| AWS Glue Data Catalog optimizers | Glue runs catalog-level compaction, snapshot retention, and orphan-file deletion for configured tables. | AWS documents restrictions for some table configurations and environments; confirm current applicability before enabling optimizers. |
| Self-managed Apache Iceberg procedures | Your chosen compute engine runs procedures for rewriting data files, expiring snapshots, and removing orphan files. Your team schedules and operates the jobs. | Your team owns orchestration, permissions, monitoring, failure handling, recovery, and safe coordination with writes. |
| Snowflake-managed Iceberg tables | Snowflake documents compaction for tables it manages. | Snowflake says orphan-file deletion is not supported for Snowflake-managed Iceberg tables. Its maintenance is not a feature-for-feature replacement for Glue. |
| Apache Iceberg procedures on AWS compute | AWS guidance discusses running Iceberg procedures using Spark on Amazon EMR or AWS Glue. | This is an execution path for maintenance jobs, not evidence of an equivalent, fully managed catalog optimizer. |
Run Iceberg maintenance yourself
Apache Iceberg provides procedures for rewriting data files, expiring snapshots, and removing orphan files. This approach can suit a team that needs to choose its compute engine or schedule, or already operates table-maintenance jobs. It does not remove the need to design and support the jobs: the team remains responsible for permissions, orchestration, observability, failures, and retention settings.
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Use Snowflake for Snowflake-managed tables
Snowflake’s documentation distinguishes its guidance for Snowflake-managed Iceberg tables from guidance for externally managed tables. Assess the table’s ownership model and required cleanup operations before choosing it. In particular, do not assume Snowflake-managed compaction includes orphan-file deletion.
Consider other AWS execution or table services separately
AWS Prescriptive Guidance discusses Apache Iceberg procedures on Amazon EMR or AWS Glue, including orphan-file removal. That guidance describes where jobs can run; it does not establish that those jobs provide the same automation as Glue’s catalog optimizers. Amazon S3 Tables is another AWS-managed Iceberg table option to assess, but its maintenance capabilities should be checked against the specific operations your workload requires rather than assumed to match Glue.
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How should you choose?
Start with table ownership, then compare the maintenance operations and operational work the option leaves to your team. A managed feature can reduce job ownership without providing every cleanup function; a procedure-based approach can offer execution flexibility while making your team responsible for safe operation.
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- Operations: Check independently for compaction, snapshot expiration, and orphan-file deletion. Verify the compaction strategies and table types supported where those details matter.
- Retention: Decide how much time-travel and rollback history to preserve. Snapshot expiration and deletion of unreferenced files are distinct operations with different consequences.
- Operational responsibility: Determine who will schedule jobs, grant permissions, monitor outcomes, handle failures, and recover from mistakes.
- Portability: Consider whether the choice constrains the catalog, compute engine, storage ownership, or ability to move maintenance elsewhere.
The official materials cited here do not establish a universal performance or cost winner. Those outcomes depend on workload and deployment, so compare options using your own table sizes, write patterns, retention needs, and operational constraints.
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What safety checks matter most?
Protect files used by active writes
Orphan-file cleanup is a deletion operation, not merely a layout optimization. Apache Iceberg warns that setting the orphan-file retention interval shorter than the time a write can take may cause files from an in-progress write to be treated as orphans and deleted, potentially corrupting the table. AWS likewise advises setting retention longer than the maximum expected interval between file creation and successful commit. Base the interval on the real upper bound for writes, processing delays, and commit retries.
Prevent overlapping tables and lifecycle policies from deleting live files
AWS warns against enabling snapshot-retention or orphan-file optimizers on catalog tables that share an S3 location: cleanup for one table can remove files still referenced by another table. Keep table paths and subpaths from overlapping with other tables or data sources. Also review S3 Lifecycle rules; AWS warns that they can delete files still referenced by active snapshots, so exclude Iceberg storage paths where needed.
Check service limits and unsupported cases
AWS Glue documentation states a maximum of 1,000,000 files deleted per run for snapshot-retention and orphan-file optimizers. This is a per-run service limit, not a performance benchmark. AWS also documents compaction limitations involving cross-account and cross-Region tables, resource links, and S3 Express One Zone Iceberg tables. Consult the current Glue considerations and limitations for the exact scope that applies to your account and table configuration.
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What to verify before implementation
- Inventory each table: Record its catalog, storage location, ownership model, write patterns, and any overlapping paths or data sources.
- Map maintenance needs: Decide whether you need compaction, snapshot expiration, orphan-file deletion, or all three; set the history required for time travel and rollback.
- Validate support: Check the current documentation for your engine, service, table type, region, and account arrangement.
- Set safe cleanup retention: Include the longest plausible write and commit delay, processing delays, and retries before defining when files can be considered orphaned.
- Assign operations ownership: For every job or managed feature, identify who monitors execution, investigates failures, and responds to an unsafe or incomplete cleanup.
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