dbt Core v1.6 has three significant areas of focus:
- Next milestone of multi-project deployments: improvements to contracts, groups/access, versions; and building blocks for cross-project
- Semantic layer re-launch: dbt Core and MetricFlow integration
- Mechanisms to support mature deployment at scale (
What to know before upgrading
dbt Labs is committed to providing backward compatibility for all versions 1.x, with the exception of any changes explicitly mentioned below. If you encounter an error upon upgrading, please let us know by opening an issue.
If your dbt project defines metrics, you must migrate to dbt v1.6 because the YAML spec has moved from dbt_metrics to MetricFlow. Any tests you have won't compile on v1.5 or older.
- dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
- As part of the dbt Semantic layer re-launch (in beta), the spec for
metricshas changed significantly. Refer to the migration guide for more info on how to migrate to the re-launched dbt Semantic Layer.
- The manifest schema version is now v10.
- dbt Labs is ending support for Homebrew installation of dbt-core and adapters. See the discussion for more details.
For consumers of dbt artifacts (metadata)
The manifest schema version has been updated to
v10. Specific changes:
- Addition of
semantic_modelsand changes to
- Addition of
deprecation_dateas a model property
- Addition of
on_configuration_changeas default node configuration (to support materialized views)
- Small type changes to
For maintainers of adapter plugins
For more detailed information and to ask questions, please read and comment on the GH discussion: dbt-labs/dbt-core#7958.
New and changed documentation
- Build your metrics with MetricFlow, a key component of the dbt Semantic Layer. You can define your metrics and build semantic models with MetricFlow, available on the command line (CLI) for dbt Core v1.6 beta or higher.
Support for BigQuery coming soon.
New commands for mature deployment
dbt retry executes the previously run command from the point of failure. Rebuild just the nodes that errored or skipped in a previous run/build/test, rather than starting over from scratch.
dbt clone leverages each data platform's functionality for creating lightweight copies of dbt models from one environment into another. Useful when quickly spinning up a new development environment, or promoting specific models from a staging environment into production.
Deprecation date: Models can declare a deprecation date that will warn model producers and downstream consumers. This enables clear migration windows for versioned models, and provides a mechanism to facilitate removal of immature or little-used models, helping to avoid project bloat.
Model names can be duplicated across different namespaces (projects/packages), so long as they are unique within each project/package. We strongly encourage using two-argument
ref when referencing a model from a different package/project.
More consistency and flexibility around packages. Resources defined in a package will respect variable and global macro definitions within the scope of that package.
varsdefined in a package's
dbt_project.ymlare now available in the resolution order when compiling nodes in that package, though CLI
--varsand the root project's
varswill still take precedence. See "Variable Precedence" for details.
generate_x_namemacros (defining custom rules for database, schema, alias naming) follow the same pattern as other "global" macros for package-scoped overrides. See macro dispatch for an overview of the patterns that are possible.
Project dependencies: Introduces
dependencies.yml and dependent
projects as a feature of dbt Cloud Enterprise. Allows enforcing model access (public vs. protected/private) across project/package boundaries. Enables cross-project
ref of public models, without requiring the installation of upstream source code.
state:oldfor MECE stateful selection
dbt debug --connectionto test just the data platform connection specified in a profile
dbt docs generate --empty-catalogto skip catalog population while generating docs
--defer-stateenables more-granular control