Create dynamic Apache Iceberg™ tables¶

Create dynamic tables that read from Iceberg tables¶

Dynamic tables that read from a Snowflake-managed Iceberg table are useful if you want your pipelines to operate on data in a Snowflake-managed Iceberg table or if you want your processed data to be queryable from other engines, such as using the Apache Iceberg SDK with Spark.

Creating a dynamic from an Iceberg table is similar to creating a dynamic table from a regular table. To do this, execute the CREATE DYNAMIC TABLE SQL statement like you would for a regular table.

Create dynamic Iceberg tables¶

Dynamic Iceberg tables combine the benefits of dynamic tables and Snowflake-managed Iceberg tables, offering features like external cloud storage management, automated data transformation, and performance optimization.

Dynamic Iceberg tables integrate with data lakes, which let you store data in external cloud storage such as AWS S3 or Azure Blob Storage while being managed by Snowflake. These tables support ACID transactions, schema evolution, hidden partitioning, and table snapshots.

Automated data transformation with dynamic Iceberg tables uses declarative SQL to define the desired end state without managing intermediary steps. Snowflake handles orchestration, scheduling, and refreshing data transformations based on your specified data freshness targets.

Performance is optimized through incremental processing, which processes only changed data to improve performance and reduce costs compared to full data refreshes. Additionally, you can transition between batch processing and streaming data with a simple command, providing flexibility in data processing workflows.

Example use cases for dynamic Iceberg tables include the following:

• Data lake integration: You can store large datasets cost-effectively while performing transformations and analytics within Snowflake, leveraging the Iceberg format for efficient querying and management.

• Defining continuous data transformation pipelines: By using dynamic tables, you can ensure data is always up to date without manual intervention and handle high-velocity data streams efficiently with incremental processing.

To create a dynamic Iceberg table, execute the CREATE DYNAMIC ICEBERG TABLE SQL statement. For example, to create a dynamic Iceberg table named product that reads from my_iceberg_table, use the following syntax:

CREATE DYNAMIC ICEBERG TABLE product (product_id NUMBER(10,0), product_name STRING, order_time TIMESTAMP_NTZ)
  TARGET_LAG = '20 minutes'
  WAREHOUSE = my_warehouse
  EXTERNAL_VOLUME = 'my_external_volume'
  CATALOG = 'SNOWFLAKE'
  BASE_LOCATION = 'my_iceberg_table'
  AS
    SELECT product_id, product_name, order_time FROM staging_table;

Future grants on dynamic Iceberg tables¶

To ensure access to any new dynamic Iceberg tables created in the schema, use the GRANT … ON FUTURE ICEBERG TABLES syntax without the DYNAMIC keyword. For example:

GRANT <privilege> ON FUTURE ICEBERG TABLES IN SCHEMA my_schema TO ROLE my_role;

If you use the DYNAMIC keyword, the grant doesn’t provide access to new dynamic Iceberg tables created in the schema. For instance, the following command doesn’t apply for dynamic Iceberg tables:

GRANT <privilege> ON FUTURE DYNAMIC TABLES IN SCHEMA my_schema TO ROLE my_role;

Considerations and limitations¶

• Dynamic Iceberg tables support the same data types as regular Iceberg tables in Snowflake. For more information, see Supported data types.

• The Catalog is an account, schema, or database parameter that you can configure to be implicit, just like regular Snowflake managed Iceberg tables.

• Dynamic Iceberg tables don’t currently support the IF NOT EXISTS clause. Using the IF NOT EXISTS clause throws an error if the target table already exists.

• Dynamic Iceberg tables are currently only supported for CREATE statements. Specifying DYNAMIC ICEBERG in any other command (for example, ALTER DYNAMIC ICEBERG TABLE <name>) results in an error.