Snowflake is broadening the reach of its AI coding assistant, Cortex Code. Initially designed with a Snowflake-centric view, the tool now aims to understand and operate across an entire data stack, including tools like dbt and Apache Airflow. This expansion addresses a key pain point for developers: AI assistants that lack the contextual awareness of data schemas, lineage, and access policies inherent in data-specific workflows.
The company announced that Cortex Code now supports additional data systems such as AWS Glue, Databricks, and Postgres. This allows data engineers to maintain context across disparate systems, reducing the time spent re-establishing context and debugging cross-system failures. The goal is a single AI agent that grasps the user's complete data footprint.
New Agent Skills for Data Engineering
Cortex Code is enhancing its capabilities with specialized agent 'skills' designed for core data engineering tasks. These include expert workflows for Snowpark Python, data quality checks, lineage tracking, and cost intelligence.
A new snowpark-python skill offers guidance throughout the Python pipeline lifecycle, from authoring code to deployment and observability. The snowpark-connect skill aims to streamline the migration of existing Apache Spark workloads to Snowflake, analyzing and fixing compatibility issues.
For those managing data transformation, the dbt-projects-on-snowflake skill provides native Snowflake object management for dbt projects. This includes deployment, versioning, documentation generation, and scheduling. Another skill, dcm, covers the full lifecycle of Declarative Configuration Management projects within Snowflake.