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dbt Semantic Layer updates and fixes for January 2024

The dbt Labs team continues to work on adding new features, fixing bugs, and increasing reliability for the dbt Semantic Layer.

The following list explains the new features, updates, and fixes for January 2024 in more detail.

New features​

  • Conversion metrics — New metric type that allows you to measure conversion events. For example, users who viewed a web page and then filled out a form. For more details, refer to Conversion metrics.
  • Simplified dimension resolution — Instead of specifying the fully qualified dimension name (for example, order__user__country) in the group by or filter expression, you now only need to provide the primary entity and dimensions name, like user__county.
  • Saved queries — You can now query the saved queries you've defined in the dbt Semantic Layer using Tableau, GraphQL API, JDBC API, and the dbt Cloud CLI.

Updates​

  • Display label for dbt Semantic Layer metrics — The YAML spec parameter label is now available for Semantic Layer metrics in JDBC and GraphQL APIs. This means you can conveniently use label as a display name for your metrics when exposing them.
  • Use shorthand to create metrics — Added support for create_metric: true for a measure, which is a shorthand to quickly create metrics. This is useful in cases when metrics are only used to build other metrics.
  • Support for Tableau parameter filters — Added support for Tableau parameter filters. You can use the Tableau connector to create and use parameters with your dbt Semantic Layer data.
  • Additional parameters for GraphQL API — Added support to expose expr and agg for Measures in the GraphQL API.
  • Improved error messages — You have improved error messages in the command line interface when querying a dimension that is not reachable for a given metric.
  • Entities in Tableau — You can now query entities using our Tableau integration (similar to querying dimensions).
  • New Tableau data source — A new data source is available in our Tableau integration called "ALL", which contains all semantic objects defined. This has the same information as "METRICS_AND_DIMENSIONS". In the future, we will deprecate "METRICS_AND_DIMENSIONS" in favor of "ALL" for clarity.

Bug fixes​

  • BigQuery numeric types — Support for numeric types with precision greater than 38 (like BIGDECIMAL) in BigQuery is now available. Previously, it was unsupported so would return an error.
  • Large numbers incorrectly displayed in scientific notation — In some instances, large numeric dimensions were being interpreted by Tableau in scientific notation, making them hard to use. These should now be displayed as numbers as expected.
  • Google Sheets dimension values — We now preserve dimension values accurately instead of being inadvertently converted into strings.
  • Multiple derived metrics resolution — Resolved issues with naming collisions in queries involving multiple derived metrics using the same metric input. Previously, this could cause a naming collision. Input metrics are now deduplicated, ensuring each is referenced only once.
  • Deduplication of input measures — Resolved warnings related to using two duplicate input measures in a derived metric. Previously, this would trigger a warning. Input measures are now deduplicated, enhancing query processing and clarity.
  • Correctly reference entities in filters using the object syntax — Resolved an error where referencing an entity in a filter using the object syntax would fail. For example, {{Entity('entity_name')}} would fail to resolve.
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