Data Maintenance With Icehouse LakeOps

Iceberg tables can gradually lose performance and accumulate storage costs when maintenance is absent or no longer matches workload growth, with common symptoms including bloated metadata, proliferating small files and delete files, slower query planning and execution, and orphaned storage objects. The post describes Starburst Icehouse LakeOps as an automated, serverless maintenance service that addresses metadata growth by rewriting manifests, expiring obsolete snapshots, and removing dangling deletes; improves query performance through compaction that combines small files and incorporates deleted records into rewritten data files; and reclaims wasted storage by detecting and deleting orphaned files left behind by expired snapshots or failed operations. It argues that continuous, incremental maintenance can keep large Iceberg tables efficient and reliable without requiring teams to schedule jobs or manage clusters manually.

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