Apache Iceberg is a powerful open table format, but has practical limitations. Learn about its challenges with small data, metadata overhead, and real-time use cases.

Quesma

Apache Iceberg is a powerful open table format enabling multi-engine data sharing, but it comes with real practical limitations in 2025. Key issues include metadata overhead and file explosion for small-scale or high-frequency writes, a fragmented ecosystem where Python/Rust/Go ports lag behind the Java reference implementation, incomplete write and delete support in popular engines like ClickHouse and DuckDB, performance penalties (2-3x slower than native formats), lack of built-in governance/security controls, and hard architectural limits around write concurrency (Adobe hit ~15 transactions/minute) and real-time ingestion. Cloud egress costs also undermine Iceberg's multi-cloud portability promise. Iceberg is best suited for large, slowly-changing datasets shared across query engines — not for OLTP, real-time observability, or small-data workloads.

Don't Let Apache Iceberg Sink Your Analytics: Practical Limitations in 2025

Iceberg Implementation Limitations in 2025