Historically, importing data into Snowflake meant locking it into Snowflake's proprietary internal storage format. To embrace the Open Data Architecture of the modern lakehouse, Snowflake introduced native support for Apache Iceberg, allowing users to query and manipulate data using open standards while maintaining Snowflake's query performance.
Architectural Flexibility
Snowflake's implementation of Iceberg tables is highly flexible, allowing organizations to decide exactly how much control they want to cede to Snowflake versus maintaining independent, open infrastructure. This is broken down into two main categories of management:
1. Snowflake-Managed Iceberg Tables
In this configuration, Snowflake acts as the Iceberg Catalog. Snowflake is responsible for maintaining the metadata, committing new snapshots, and executing background maintenance like compaction.
- Storage Options: By 2026, Snowflake enabled organizations to store these managed Iceberg tables either on their own External Volumes (like an AWS S3 bucket owned by the customer) or on Snowflake-Managed Storage.
- Interoperability: Because the data is written in the open Iceberg specification, external compute engines (like Apache Spark or Dremio) can read these tables directly from the storage bucket, even though Snowflake is the primary writer.
2. Unmanaged (Externally Managed) Iceberg Tables
In this configuration, the organization uses an external catalog (such as AWS Glue, Project Nessie, or Apache Polaris) as the source of truth for the Iceberg table. Snowflake connects to this external catalog to read the metadata.
- Use Case: This is ideal for organizations executing heavy ETL pipelines outside of Snowflake (e.g., streaming data via Apache Flink) but who still want business analysts to query the resulting data using Snowflake's high-performance warehouse engine.
- Read vs. Write: Historically, externally managed tables in Snowflake were read-only, but as the Iceberg REST catalog specification matured, cross-engine read/write capabilities became increasingly viable across the ecosystem.
Benefits of Snowflake Iceberg
By utilizing Snowflake Iceberg Tables, organizations prevent vendor lock-in. They benefit from Snowflake's security, governance, and SQL optimizations without having to duplicate massive datasets or pay expensive data egress fees if they decide to use a different compute engine for a specific machine learning workload.
