dbt for Treasure Data

关于

This skill empowers developers to build, test, and deploy robust data models on Treasure Data using dbt. It offers specialized support for configuring connection profiles, implementing incremental models using TD-specific time functions like TD_INTERVAL, and applying necessary macro overrides to handle Trino limitations within the Treasure Data environment. From project initialization with modern package managers like uv to troubleshooting adapter errors and managing complex data pipelines, this skill ensures your data engineering workflows follow Treasure Data best practices.

主要功能

• 2 GitHub stars
• Specialized profiles and project configuration guidance for Treasure Data connectivity
• Implementation of incremental models using TD_INTERVAL and custom scanning macros
• Best practices for schema evolution and source definitions in TD environments
• Required macro overrides to handle Trino limitations such as view creation restrictions
• Performance optimization patterns using TD-specific time filters and session properties

使用场景

• Configuring a new dbt project to connect securely to Treasure Data's Trino engine
• Converting standard dbt models into optimized incremental models for high-volume event data
• Troubleshooting adapter-specific errors such as catalog query failures or view support issues