Amazon Redshift will no longer support the use of Python UDFs after June 30, 2026. We will start enforcing it in phases. For more information on the details of Python end of life and migration options, see the [ blog post ](https://amazonaws-china.com/blogs/big-data/amazon-redshift-python-user-defined-functions-will-reach-end-of-support-after-june-30-2026/) that was published on June 30, 2025. 

# Using Amazon Sagemaker Unified Studio to query your databases in Amazon Redshift and the SageMaker lakehouse
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Amazon SageMaker Unified Studio provides an off-console development environment and supports SQL analytics on data in the SageMaker lakehouse, Amazon Redshift, and Amazon Athena for SQL analytics. Navigate to Amazon SageMaker Unified Studio using the URL from your admin and use your SSO or Amazon credentials to log in. For more information about setting up your first project, see [Getting started](https://docs.amazonaws.cn/sagemaker-unified-studio/latest/userguide/getting-started.html) in the *Amazon SageMaker Unified Studio User Guide*.

In Amazon SageMaker Unified Studio, you can perform [SQL analytics](https://docs.amazonaws.cn/sagemaker-unified-studio/latest/userguide/sql-query.html) by running Amazon Redshift and Amazon Athena with the [query editor](https://docs.amazonaws.cn/sagemaker-unified-studio/latest/userguide/query-editor-navigate.html). Use the query editor to write and run queries, view results, and share your work with your team. Run queries against your Redshift data warehouses in your Amazon Web Services accounts (within the same account and across your other Amazon Web Services accounts), build SQL queries for both Redshift and Athena using the same interface and schedule the SQL queries using Amazon Managed Workflows for Apache Airflow. You can also use Amazon Q generative SQL to generate SQL from natural language.