Apache Iceberg™ tables

Apache Iceberg™ tables for Snowflake combine the performance and query semantics of typical Snowflake tables with external cloud storage that you manage. They are ideal for existing data lakes that you cannot, or choose not to, store in Snowflake.

Iceberg tables use the Apache Iceberg™ open table format specification, which provides an abstraction layer on data files stored in open formats and supports features such as:

  • ACID (atomicity, consistency, isolation, durability) transactions

  • Schema evolution

  • Hidden partitioning

  • Table snapshots

Snowflake supports Iceberg tables that use the Apache Parquet™ file format.

Getting started

To get started with Iceberg tables, see Tutorial: Create your first Apache Iceberg™ table.

How it works

This section provides information specific to working with Iceberg tables in Snowflake. To learn more about the Iceberg table format specification, see the official Apache Iceberg documentation and the Iceberg Table Spec.

Data storage

Iceberg tables store their data and metadata files in an external cloud storage location (Amazon S3, Google Cloud Storage, or Azure Storage). The external storage is not part of Snowflake. You are responsible for all management of the external cloud storage location, including the configuration of data protection and recovery. Snowflake does not provide Fail-safe storage for Iceberg tables.

Snowflake connects to your storage location using an external volume, and Iceberg tables incur no Snowflake storage costs. For more information, see Billing.

To learn more about storage for Iceberg tables, see Storage for Apache Iceberg™ tables.

External volume

An external volume is a named, account-level Snowflake object that you use to connect Snowflake to your external cloud storage for Iceberg tables. An external volume stores an identity and access management (IAM) entity for your storage location. Snowflake uses the IAM entity to securely connect to your storage for accessing table data, Iceberg metadata, and manifest files that store the table schema, partitions, and other metadata.

A single external volume can support one or more Iceberg tables.

To set up an external volume for Iceberg tables, see Configure an external volume.

Catalog

An Iceberg catalog enables a compute engine to manage and load Iceberg tables. The catalog forms the first architectural layer in the Iceberg table specification and must support:

  • Storing the current metadata pointer for one or more Iceberg tables. A metadata pointer maps a table name to the location of that table’s current metadata file.

  • Performing atomic operations so that you can update the current metadata pointer for a table.

To learn more about Iceberg catalogs, see the Apache Iceberg documentation.

Snowflake supports different catalog options. For example, you can use Snowflake as the Iceberg catalog, or use a catalog integration to connect Snowflake to an external Iceberg catalog.

Catalog integration

A catalog integration is a named, account-level Snowflake object that stores information about how your table metadata is organized for the following scenarios:

  • When you don’t use Snowflake as the Iceberg catalog. For example, you need a catalog integration if your table is managed by AWS Glue.

  • When you want to integrate with Snowflake Open Catalog to:

    • Query an Iceberg table in Snowflake Open Catalog using Snowflake.

    • Sync a Snowflake-managed Iceberg table with Snowflake Open Catalog so that third-party compute engines can query the table.

A single catalog integration can support one or more Iceberg tables that use the same external catalog.

To set up a catalog integration, see Configure a catalog integration.

Metadata and snapshots

Iceberg uses a snapshot-based querying model, where data files are mapped using manifest and metadata files. A snapshot represents the state of a table at a point in time and is used to access the complete set of data files in the table.

To learn about table metadata and Time Travel support, see Metadata and retention for Apache Iceberg™ tables.

Cross-cloud/cross-region support

Cross-cloud/cross-region support depends on the type of Iceberg table.

Table type

Cross-cloud/cross-region support

Notes

Tables that use an external catalog with a catalog integration

✔

If the active storage location for your external volume is not with the same cloud provider or in the same region as your Snowflake account, the following limitations apply:

  • You can’t use the SYSTEM$GET_ICEBERG_TABLE_INFORMATION function to retrieve information about the latest refreshed snapshot.

  • You can’t convert the table to use Snowflake as the catalog.

If your Snowflake account and external volume are in different regions, your external cloud storage account incurs egress costs when you query the table.

Tables that use Snowflake as the catalog

❌

Your external volume must use an active storage location with the same cloud provider (in the same region) that hosts your Snowflake account.

If the active location is not in the same region, the CREATE ICEBERG TABLE statement returns a user error.

Billing

Snowflake bills your account for virtual warehouse (compute) usage and cloud services when you work with Iceberg tables. Snowflake also bills your account if you use automated refresh.

Snowflake does not bill your account for the following:

Note

If your Snowflake account and external volume are in different regions, your external cloud storage account incurs egress costs when you query the table.

Catalog options

Snowflake supports the following Iceberg catalog options:

The following table summarizes the differences between these catalog options.

Use Snowflake as the catalog

Use an external catalog

Read access

✔

✔

Write access

✔

❌

For Snowflake platform support, you can convert the table to use Snowflake as the catalog.

Data and metadata storage

External volume (cloud storage)

External volume (cloud storage)

Snowflake platform support

✔

❌

Integrates with Snowflake Open Catalog

✔

You can sync a Snowflake-managed table with Open Catalog to query a table using other compute engines.

✔

You can use Snowflake to query Iceberg tables managed by Open Catalog.

Works with the Snowflake Catalog SDK

✔

✔

Use Snowflake as the catalog

An Iceberg table that uses Snowflake as the Iceberg catalog (Snowflake-managed Iceberg table) provides full Snowflake platform support with read and write access. The table data and metadata are stored in external cloud storage, which Snowflake accesses using an external volume. Snowflake handles all life-cycle maintenance, such as compaction, for the table.

How Iceberg tables that use Snowflake as the Iceberg catalog work

Use an external catalog

An Iceberg table that uses an external catalog provides limited Snowflake platform support with read-only access. With this table type, Snowflake uses a catalog integration to retrieve information about your Iceberg metadata and schema.

You can use this option to create an Iceberg table for the following sources:

  • AWS Glue Data Catalog

  • Iceberg metadata files in object storage

  • Delta table files in object storage

  • Open Catalog

  • Remote Iceberg REST catalog

Snowflake does not assume any life-cycle management on the table.

The table data and metadata are stored in external cloud storage, which Snowflake accesses using an external volume.

The following diagram shows how an Iceberg table uses a catalog integration with an external Iceberg catalog.

How Iceberg tables that use a catalog integration work

Considerations and limitations

The following considerations and limitations apply to Iceberg tables, and are subject to change:

Clouds and regions

  • Iceberg tables are available for all Snowflake accounts, on all cloud platforms and in all regions except in the China region.

  • Cross-cloud/cross-region tables are supported when you use an external catalog. For more information, see Cross-cloud/cross-region support.

Iceberg

  • Versions 1 and 2 of the Apache Iceberg specification are supported, excluding the following features:

    • Row-level deletes (either position deletes or equality deletes). However, tables that use Snowflake as the catalog support Snowflake DELETE statements.

    • Using the history.expire.min-snapshots-to-keep table property to specify the default minimum number of snapshots to keep. For more information, see Metadata and snapshots.

  • Iceberg partitioning with the bucket transform function impacts performance for queries that use conditional clauses to filter results.

  • For Iceberg tables that aren’t managed by Snowflake, be aware of the following:

    • Time travel to any snapshot generated after table creation is supported as long as you periodically refresh the table before the snapshot expires.

    • Converting a table that has an un-materialized identity partition column isn’t supported. An un-materialized identity partition column is created when a table defines an identity transform using a source column that doesn’t exist in a Parquet file.

File formats

  • Iceberg tables support Apache Parquet files.

  • Parquet files that use the unsigned integer logical type aren’t supported.

External volumes

  • You can’t access the cloud storage locations in external volumes using a storage integration.

  • You must configure a separate trust relationship for each external volume that you create.

Metadata files

  • The metadata files don’t identify the most recent snapshot of an Iceberg table.

  • You can’t modify the location of the data files or snapshot using the ALTER ICEBERG TABLE command. To modify either of these settings, you must recreate the table (using the CREATE OR REPLACE ICEBERG TABLE syntax).

  • For tables that use an external catalog:

    • Ensure that manifest files don’t contain duplicates. If duplicate files are present in the same snapshot, Snowflake returns an error that includes the path of the duplicate file.

    • You can’t create a table if the Parquet metadata contains invalid UTF-8 characters. Ensure that your Parquet metadata is UTF-8 compliant.

  • Snowflake detects corruptions and inconsistencies in Parquet metadata produced outside of Snowflake, and surfaces issues through error messages.

    It’s possible to create, refresh, or query externally managed (or converted) tables, even if the table metadata is inconsistent. When writing Iceberg data, ensure that the table’s metadata statistics (for example, RowCount or NullCount) match the data content.

  • For tables that use Snowflake as the catalog, Snowflake processes DDL statements individually and produces metadata in a way that might differ from other catalogs. For more information, see DDL statements.

Clustering

Clustering support depends on the type of Iceberg table.

Table type

Notes

Tables that use Snowflake as the Iceberg catalog

Set a clustering key by using either the CREATE ICEBERG TABLE or the ALTER ICEBERG TABLE command. To set or manage a clustering key, see CREATE ICEBERG TABLE (Snowflake as the Iceberg catalog) and ALTER ICEBERG TABLE.

Tables that use an external catalog

Clustering is not supported.

Converted tables

Snowflake only clusters files if they were created after converting the table, or if the files have since been modified using a DML statement.

Delta

  • Snowflake streams aren’t supported for Iceberg tables created from Delta table files with partition columns. However, insert-only streams for tables created from Delta files without partition columns are supported.

  • Dynamic tables aren’t supported on Iceberg tables created from Delta table files.

  • Snowflake doesn’t support creating Iceberg tables from Delta table definitions in the AWS Glue Data Catalog.

  • Parquet files (data files for Delta tables) that use any of the following features or data types aren’t supported:

    • Field IDs.

    • The INTERVAL data type.

    • The DECIMAL data type with precision higher than 38.

    • LIST or MAP types with one-level or two-level representation.

    • Unsigned integer types (INT(signed = false)).

    • The FLOAT16 data type.

    For more information about Delta data types and Iceberg tables, see Delta data types.

  • Refresh operations during CREATE and ALTER … REFRESH can process a maximum of 1,000 Delta commit files per operation.

    Note

    Snowflake uses Delta checkpoint files when creating an Iceberg table. The 1,000 commit file limit only applies to commits after the latest checkpoint.

  • Generating Iceberg metadata using the SYSTEM$GET_ICEBERG_TABLE_INFORMATION function isn’t supported.

  • The following Delta Lake features aren’t currently supported: Row tracking, deletion vector files, change data files, change metadata, DataChange, CDC, protocol evolution.

Automated refresh

  • When automated refresh is enabled, you can’t manually refresh the table metadata. To perform a manual refresh, turn off automated refresh first.

  • For catalog integrations created before Snowflake version 8.22, you must manually set the REFRESH_INTERVAL_SECONDS parameter before you enable automated refresh on tables that depend on that catalog integration. For instructions, see ALTER CATALOG INTEGRATION … SET AUTO_REFRESH.

  • Ensure that the new table snapshot is a direct child of the current table snapshot. Otherwise, automated refresh enters the STOPPED state. To recover automated refresh when this occurs, see Error recovery.

  • Automated refresh isn’t supported when you use a catalog integration for object storage.

  • Iceberg version 1 manifests without a sequence number column aren’t currently supported.

Access by third-party clients to Iceberg data, metadata

  • Third-party clients can’t append to, delete from, or upsert data to Iceberg tables that use Snowflake as the catalog.

Unsupported features

The following Snowflake features aren’t currently supported for all Iceberg tables:

The following features aren’t supported for Iceberg tables that use an external catalog:

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