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Adding proactive release notes
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rpourzand authored Jul 23, 2024
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- **New**: Introduced the [`dbt-sl-sdk` Python software development kit (SDK)](https://github.com/dbt-labs/semantic-layer-sdk-python) Python library, which provides you with easy access to the dbt Semantic Layer with Python. It allows developers to interact with the dbt Semantic Layer APIs and query metrics and dimensions in downstream tools. Refer to the [dbt Semantic Layer Python SDK](/docs/dbt-cloud-apis/sl-python) for more information.
- **New**: The ability to lint your SQL files from the dbt Cloud CLI is now available. To learn more, refer to [Lint SQL files](/docs/cloud/configure-cloud-cli#lint-sql-files).
- **New**: Introduced Semantic validations in CI pipelines. Automatically test your semantic nodes (metrics, semantic models, and saved queries) during code reviews by adding warehouse validation checks in your CI job using the `dbt sl validate` command. You can also validate modified semantic nodes to guarantee code changes made to dbt models don't break these metrics. Refer to [Semantic validations in CI](/docs/deploy/ci-jobs#semantic-validations-in-ci) to learn about the additional commands and use cases.
- **New**: We now expose the `meta` field within the the [config property](https://docs.getdbt.com/reference/resource-configs/meta) for dbt Semantic Layer metrics in the JDBC and GraphQL APIs under the field `meta`.
- **New**: We added a command in the Cloud CLI for called `export-all`, which allows you Export multiple or all of your saved queries. Previously, you had to explicitly specify the list of saved queries (need to link docs)
- **Fix**: We fixed a bug where unicode query filters (such as Chinese) were not working correctly in the dbt Semantic Layer Tableau integration.
- **Fix**: We fixed a bug where certain private keys for BigQuery were not being parsed correctly when running an Export
- **Fix**: We fixed a bug that caused a "closed connection" error to be returned when querying or running an Export.


## June 2024
- **New:** Introduced new granularity support for cumulative metrics in MetricFlow. Granularity options for cumulative metrics are slightly different than granularity for other metric types. For other metrics, we use the `date_trunc` function to implement granularity. However, because cumulative metrics are non-additive (values can't be added up), we can't use the `date_trunc` function to change their time grain granularity.
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