BerriAI / litellm
github.com·3d ago
Developer Tools
Databricks Blog·Aug 4·5 min read
Granular Usage Attribution for dbt Pipelines with Query Tags - Cloned
TL;DR
Query Tags for dbt pipelines enhance query tracking by automatically tagging each run with relevant metadata, improving cost attribution and performance analysis.
✦ Why It Matters
Start using Query Tags today to improve cost tracking and performance analysis in your dbt projects.
Key Takeaways
- Implement Query Tags for automatic query metadata tracking.
- Use the dbt-databricks adapter for seamless integration.
- Analyze compute time per model to identify optimization opportunities.
- Leverage dashboards for real-time cost monitoring and insights.
How It Works
Query Tags are automatically injected into every query executed by dbt models, providing metadata such as model names and materialization strategies. Users can also define custom tags at both project and model levels, allowing for granular tracking of resource usage and costs.
This data is stored in system.query.history, enabling straightforward SQL queries to extract insights.