Yes, Snowflake supports Iceberg tables, with a specific requirement regarding the underlying data format.
Snowflake's Integration with Apache Iceberg
Snowflake offers capabilities to interact with data stored externally in the Apache Iceberg table format. This integration is valuable for organizations managing large data lakes outside of Snowflake but wanting to leverage Snowflake's query engine for analysis.
A crucial aspect of Snowflake's support for Iceberg tables is the format of the data files. As specified, Snowflake supports Iceberg tables that use the Apache Parquet™ file format. This means that for Snowflake to effectively query and interact with your Iceberg data, the actual data files stored in your cloud storage (such as Amazon S3, Google Cloud Storage, or Azure Data Lake Storage) must be in the highly popular columnar Parquet format.
Key Considerations for Snowflake and Iceberg:
- File Format Requirement: The most significant point is the necessity for underlying data files to be in Apache Parquet format. Iceberg is an open table format that can manage data files in various formats (like Parquet, ORC, Avro), but Snowflake's current support specifically mandates Parquet.
- External Data Access: This integration allows Snowflake to act as a query engine over your existing Iceberg data lake without requiring you to ingest the data directly into Snowflake's managed storage.
- Schema Evolution and Partitioning: Iceberg's features like schema evolution and hidden partitioning are designed to be compatible with external query engines, which Snowflake leverages. However, the exact level of support for all Iceberg features might depend on the specific Snowflake version and configuration.
In summary, while Snowflake fully embraces the concept of integrating with external data lakes via formats like Iceberg, the practical implementation requires adherence to the Parquet file format for the underlying data files.
