Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Amazon Redshift will no longer support the creation of new Python UDFs starting November 1, 2025. If you would like to use Python UDFs, create the UDFs prior to that date. Existing Python UDFs will continue to function as normal. For more information, see the blog post .

Referencing Iceberg tables in Amazon Redshift

Amazon Redshift provides multiple ways to reference Apache Iceberg tables stored in your data lake. You can use external schemas to create references to Data Catalog databases containing Iceberg tables, or use three-part notation for direct access to auto-mounted catalogs.

Using external schemas to reference Iceberg tables

External schemas provide a way to reference tables in your Data Catalog from within Amazon Redshift. When you create an external schema, you establish a connection between your Amazon Redshift database and a specific Data Catalog database that contains your Iceberg tables.

To create an external schema for Iceberg tables:

CREATE EXTERNAL SCHEMA schema_name
FROM DATA CATALOG
DATABASE 'glue_database_name'
IAM_ROLE 'arn:aws:iam::account-id:role/role-name';

After creating the external schema, you can query Iceberg tables using two-part notation:

SELECT * FROM schema_name.iceberg_table_name;

You can also join Iceberg tables with local Amazon Redshift tables:

SELECT r.customer_id, i.order_date, r.customer_name
FROM local_customers r
JOIN schema_name.iceberg_orders i 
ON r.customer_id = i.customer_id;

Using three-part notation with auto-mounted catalogs

Three-part notation allows you to directly reference tables in auto-mounted catalogs without creating external schemas. This method is particularly useful when working with Amazon S3 table buckets federated with Amazon Lake Formation. For information about setting up automatic mounting of the Data Catalog, see Simplify external object access in Amazon Redshift using automatic mounting of the Amazon Glue Data Catalog.

The syntax for three-part notation is:

"catalog_name".database_name.table_name

For example, to query an Iceberg table in an auto-mounted Amazon S3 table catalog:

SELECT * FROM "my_table_bucket@s3tablescatalog".my_database.my_iceberg_table;

For more information about integrating Amazon S3 table buckets with Amazon Redshift, see Integrating S3 Tables with Amazon Redshift in the Amazon S3 User Guide.

You can also use the USE statement to set a default catalog and database:

USE "my_table_bucket@s3tablescatalog".my_database;
SELECT * FROM my_iceberg_table;

To set a search path for schema resolution:

USE "my_table_bucket@s3tablescatalog";
SET search_path TO my_database;
SELECT * FROM my_iceberg_table;

Best practices for referencing Iceberg tables

Consider the following best practices when referencing Iceberg tables in Amazon Redshift:

Common referencing patterns

The following examples demonstrate common patterns for referencing Iceberg tables:

Aggregating data across multiple Iceberg tables:

SELECT 
    region,
    SUM(sales_amount) as total_sales,
    COUNT(*) as transaction_count
FROM data_lake.sales_transactions
WHERE transaction_date >= '2024-01-01'
GROUP BY region
ORDER BY total_sales DESC;

Joining Iceberg tables with local Amazon Redshift tables:

SELECT 
    c.customer_name,
    c.customer_tier,
    SUM(o.order_amount) as total_orders
FROM customers c
JOIN data_lake.order_history o ON c.customer_id = o.customer_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY c.customer_name, c.customer_tier;

Using three-part notation with complex queries:

WITH recent_orders AS (
    SELECT customer_id, order_date, order_amount
    FROM "analytics_bucket@s3tablescatalog".ecommerce.orders
    WHERE order_date >= CURRENT_DATE - INTERVAL '7 days'
)
SELECT 
    customer_id,
    COUNT(*) as order_count,
    AVG(order_amount) as avg_order_value
FROM recent_orders
GROUP BY customer_id
HAVING COUNT(*) > 1;