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SAO deleted tables (#8458)
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website/docs/docs/dbt-versions/release-notes.md

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## January 2026
2020

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- **Enhancement:** State-aware orchestration now detects and rebuilds models whose tables are deleted from the warehouse, even when there are no code or data changes. Previously, tables deleted externally were not detected, and therefore not rebuilt, unless code or data had changed. For more information, see [Handling deleted tables](/docs/deploy/state-aware-about#handling-deleted-tables).
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- **Enhancement**: The <Constant name="fusion"/> CLI now automatically reads environment variables from a `.env` file in your current working directory (the folder you `cd` into and run dbt commands from in your terminal), if one exists. This provides a simple way to manage credentials and configuration without hardcoding them in your `profiles.yml`. The [dbt VS Code extension](/docs/about-dbt-extension) also supports `.env` files as well as <Term id="lsp" />-powered features. For more information, refer to [Install <Constant name="fusion"/> CLI](/docs/fusion/install-fusion-cli#environment-variables).
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- **New**: The new <Constant name="semantic_layer"/> YAML specification in the <Constant name="fusion_engine" /> creates an open standard for defining metrics and dimensions that works across multiple platforms.
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website/docs/docs/deploy/state-aware-about.md

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To prevent a job from being built too frequently even when the code or data state has changed, you can reduce build frequency by using the `build_after` config. For information on how to use `build_after`, refer to [Model freshness](/reference/resource-configs/freshness) and [Advanced configurations](/docs/deploy/state-aware-setup#advanced-configurations).
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### Handling deleted tables
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State-aware orchestration detects and rebuilds models when their tables are deleted in the warehouse, even if there are no code or data changes.
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When a table is deleted in the warehouse:
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- dbt raises a warning that the expected table is missing.
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- The affected model is queued for rebuild during the current run, even if there are no code or data changes.
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This behavior ensures consistency between the dbt state and the actual warehouse state. It also reduces the need to manually clear cache or disable state-aware orchestration when models are modified outside of dbt.
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## Efficient testing in state-aware orchestration <Lifecycle status="private_beta" />
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:::info Private beta feature

website/docs/docs/deploy/state-aware-setup.md

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This way, if either `dim_wizards` or `dim_worlds` has fresh upstream data and enough time passed, dbt rebuilds the models. This method helps when the need for fresher data outweighs the costs.
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## Limitation
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The following section lists considerations when using state-aware-orchestration:
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### Deleted tables
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If a table was deleted in the warehouse, and neither the model’s code nor the data it depends on has changed, state-aware orchestration does not detect a change and will not rebuild the table. This is because dbt decides what to build based on code and data changes, not by checking whether every table still exists. To build the table, you have the following options:
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- **Clear cache and rebuild**: Go to **Orchestration** > **Environments** and click **Clear cache**. The next run will rebuild all models from a clean state.
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- **Temporarily disable state-aware orchestration**: Go to **Orchestration** > **Jobs**. Select your job and click **Edit**. Under **Enable Fusion cost optimization features**, disable **State-aware orchestration** and click **Save**. Run the job to force a full build, then re‑enable the feature after the run.
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## Related docs
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- [State-aware orchestration configuration](/docs/deploy/state-aware-about)

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