dbt Core v1.11 has officially launched, marking a significant update for users looking to standardize data transformations. A key feature in this release is support for user-defined functions (UDFs). These allow users to create and manage custom functions directly within their dbt projects, extending the functionality beyond the existing macro system. UDFs enable the reuse of transformation logic across different parts of a data ecosystem, improving efficiency and consistency. This feature has been a long time coming, with community discussions dating back to a 2016 request from CEO Tristan Handy. The release includes the ability to define UDFs that can run Python code in supported environments like Snowflake and BigQuery. This opens the door for more complex logic that might be cumbersome to implement in SQL. Users can also define additional configurations for their UDFs, such as default values for arguments and volatility settings that describe function predictability. Support for UDFs varies by platform, with certain limitations depending on the adapter used. Another significant change in v1.11 is the tightening of the authoring experience. The dbt language now adheres to a set of strongly-typed JSON schemas, which helps users catch configuration issues earlier in the development process. Users will receive warnings for common pitfalls, such as misspellings or deprecated config keys. This approach aims to ensure that projects behave as expected, reducing the risk of subtle errors that only surface later in the development cycle. Custom configurations are still allowed but must now be placed under a designated meta key. Overall, v1.11 enhances both functionality and reliability for dbt users.