Pain Point

Too many small objects in S3 degrade query performance and increase API call costs. Each file requires a separate GET request, and S3 charges per-requ...

Slow first-query performance against S3-stored data, caused by object discovery, metadata fetching, and data transfer over HTTP.

Changing data schemas (adding columns, renaming fields, altering types) in S3-stored datasets without breaking downstream consumers.

Older ETL systems designed for HDFS or traditional databases that cannot efficiently write to modern S3-based lakehouse architectures.

The expense of running LLM/ML inference via cloud APIs (per-token or per-request pricing) against S3 data at scale.

The slowness and cost of listing large numbers of objects in S3's flat namespace using prefix-based scans. Paginated at 1,000 objects per request.

Table format metadata (manifests, snapshots, statistics) grows as S3 datasets grow, eventually slowing planning, compaction, and garbage collection.

The difficulty of efficiently skipping irrelevant S3 objects during queries. Requires careful partitioning strategy, predicate pushdown, and metadata ...

Dependence on a single S3 provider's proprietary features, pricing, or integrations that makes migration difficult.

The cost charged by cloud providers for data transferred out of their S3 service — to the internet, another region, or another cloud.

The differences in consistency guarantees across S3-compatible storage providers. AWS S3 is now strongly consistent; other providers may differ.

The S3 API has no atomic rename operation. Renaming requires copy-then-delete — a two-step, non-atomic process.