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Snowflake Iceberg Tables Let You Own Your Data Without Ditching Managed Services

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Snowflake's managed Iceberg tables allow engineers to store actual Parquet data files in their own cloud storage (S3 or GCS) while still letting Snowflake handle metadata management, compaction, and schema evolution. Traditional Snowflake tables use a proprietary format that locks data behind the platform, forcing costly UNLOAD operations and creating catalog drift when teams try to integrate external tools like AWS Glue or Athena. By declaring an ICEBERG table with an external volume at creation time, teams decouple storage from compute, meaning other engines like Trino or DuckDB can query the same data directly without any export jobs. This approach reduces vendor lock-in risk, since migrating away no longer requires a complex data extraction strategy — the open Iceberg spec is already readable by standard query engines. However, experts caution that managed Iceberg tables involve trade-offs and are not suitable as a blanket replacement for every table in a Snowflake environment.

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