October 2022 Summaries

Preset has integrated with dbt's Semantic Layer to facilitate the discovery and collaboration on metric definitions within Preset, allowing users to utilize dbt metrics in Apache Superset. This integration enables metrics to be defined once, controlled under source code, and reused consistently across different tools, with metrics being computed on-the-fly for flexibility in exploration. Metrics in dbt are defined in YAML files and can be translated into SQL expressions, with dbt metrics allowing for associated filters and dimensions. Preset's integration supports synchronization of dbt models and metrics into the Preset workspace using the Preset CLI, enabling seamless exploration and visualization in Superset. This process is currently set up through pipelines like GitHub Actions, but future improvements aim to simplify this by allowing configuration directly from the browser and enhancing the integration with dbt Cloud's Semantic Layer for a more metric-centric approach to data exploration.