Merge branch 'service-principal' of https://github.com/dbt-labs/docs.…

…getdbt.com into service-principal

website/docs/docs/build/custom-aliases.md

@@ -157,3 +157,8 @@ If these models should indeed have the same database identifier, you can work ar
 
 By default, dbt will create versioned models with the alias `<model_name>_v<v>`, where `<v>` is that version's unique identifier. You can customize this behavior just like for non-versioned models by configuring a custom `alias` or re-implementing the `generate_alias_name` macro.
 
+## Related docs
+
+- [Customize dbt models database, schema, and alias](/guides/customize-schema-alias?step=1) to learn how to customize dbt models database, schema, and alias
+- [Custom schema](/docs/build/custom-schemas) to learn how to customize dbt schema 
+- [Custom database](/docs/build/custom-databases) to learn how to customize dbt database

website/docs/docs/build/custom-databases.md

@@ -98,3 +98,9 @@ See docs on macro `dispatch`: ["Managing different global overrides across packa
 ### BigQuery
 
 When dbt opens a BigQuery connection, it will do so using the `project_id` defined in your active `profiles.yml` target. This `project_id` will be billed for the queries that are executed in the dbt run, even if some models are configured to be built in other projects.
+
+## Related docs
+
+- [Customize dbt models database, schema, and alias](/guides/customize-schema-alias?step=1) to learn how to customize dbt models database, schema, and alias
+- [Custom schema](/docs/build/custom-schemas) to learn how to customize dbt model schema
+- [Custom aliases](/docs/build/custom-aliases) to learn how to customize dbt model alias name

website/docs/docs/build/custom-schemas.md

@@ -207,3 +207,9 @@ In the `generate_schema_name` macro examples shown in the [built-in alternative
 If your schema names are being generated incorrectly, double-check your target name in the relevant environment.
 
 For more information, consult the [managing environments in dbt Core](/docs/core/dbt-core-environments) guide.
+
+## Related docs
+
+- [Customize dbt models database, schema, and alias](/guides/customize-schema-alias?step=1) to learn how to customize dbt models database, schema, and alias
+- [Custom database](/docs/build/custom-databases) to learn how to customize dbt model database
+- [Custom aliases](/docs/build/custom-aliases) to learn how to customize dbt model alias name

website/docs/docs/build/data-tests.md

@@ -73,7 +73,9 @@ having total_amount < 0
 
 The name of this test is the name of the file: `assert_total_payment_amount_is_positive`. 
 
-Note, you won't need to include semicolons (;) at the end of the SQL statement in your singular test files as it can cause your test to fail.
+Note:
+- Omit semicolons (;) at the end of the SQL statement in your singular test files, as they can cause your test to fail.
+- Singular tests placed in the tests directory are automatically executed when running `dbt test`. Don't reference singular tests in `model_name.yml`, as they are not treated as generic tests or macros, and doing so will result in an error.
 
 To add a description to a singular test in your project, add a `.yml` file to your `tests` directory, for example, `tests/schema.yml` with the following content:
 

website/docs/docs/build/unit-tests.md

@@ -10,9 +10,6 @@ keywords:
 
 <VersionCallout version="1.8" />
 
-
-
-
 Historically, dbt's test coverage was confined to [“data” tests](/docs/build/data-tests), assessing the quality of input data or resulting datasets' structure. However, these tests could only be executed _after_ building a model. 
 
 Starting in dbt Core v1.8, we have introduced an additional type of test to dbt - unit tests. In software programming, unit tests validate small portions of your functional code, and they work much the same way here. Unit tests allow you to validate your SQL modeling logic on a small set of static inputs _before_ you materialize your full model in production. Unit tests enable test-driven development, benefiting developer efficiency and code reliability. 
@@ -219,10 +216,19 @@ dbt test --select test_is_valid_email_address
 
 Your model is now ready for production! Adding this unit test helped catch an issue with the SQL logic _before_ you materialized `dim_customers` in your warehouse and will better ensure the reliability of this model in the future. 
 
-
 ## Unit testing incremental models
 
-When configuring your unit test, you can override the output of macros, vars, or environment variables. This enables you to unit test your incremental models in "full refresh" and "incremental" modes. 
+When configuring your unit test, you can override the output of macros, vars, or environment variables. This enables you to unit test your incremental models in "full refresh" and "incremental" modes.
+
+:::note
+Incremental models need to exist in the database first before running unit tests or doing a `dbt build`. Use the [`--empty` flag](/reference/commands/build#the---empty-flag) to build an empty version of the models to save warehouse spend. You can also optionally select only your incremental models using the [`--select` flag](/reference/node-selection/syntax#shorthand).
+
+  ```shell
+  dbt run --select "config.materialized:incremental" --empty
+  ```
+
+  After running the command, you can then perform a regular `dbt build` for that model and then run your unit test.
+:::
 
 When testing an incremental model, the expected output is the __result of the materialization__ (what will be merged/inserted), not the resulting model itself (what the final table will look like after the merge/insert).
 

website/docs/docs/cloud-integrations/semantic-layer/excel.md

@@ -36,7 +36,7 @@ import Tools from '/snippets/_sl-excel-gsheets.md';
 
 <Tools 
 type="Microsoft Excel"
-bullet_1="There's a timeout of 1 minute for queries."
+bullet_1="Results that take longer than one minute to load into Excel will fail. This limit only applies to the loading process, not the time it takes for the data platform to run the query."
 bullet_2="If you're using this extension, make sure you're signed into Microsoft with the same Excel profile you used to set up the Add-In. Log in with one profile at a time as using multiple  profiles at once might cause issues."
 queryBuilder="/img/docs/dbt-cloud/semantic-layer/query-builder.png"
 />

website/docs/docs/dbt-versions/compatible-track-changelog.md

@@ -18,6 +18,48 @@ Starting in January 2025, each monthly "Extended" release will match the previou
 
 For more information, see [release tracks](/docs/dbt-versions/cloud-release-tracks).
 
+## January 2025
+
+Release date: January 14, 2025
+
+This release includes functionality from the following versions of dbt Core OSS:
+```
+dbt-core==1.9.1
+
+# shared interfaces
+dbt-adapters==1.13.1
+dbt-common==1.14.0
+dbt-semantic-interfaces==0.7.4
+
+# adapters
+dbt-athena==1.9.0
+dbt-bigquery==1.9.1
+dbt-databricks==1.9.1
+dbt-fabric==1.9.0
+dbt-postgres==1.9.0
+dbt-redshift==1.9.0
+dbt-snowflake==1.9.0
+dbt-spark==1.9.0
+dbt-synapse==1.8.2
+dbt-teradata==1.9.0
+dbt-trino==1.9.0
+```
+
+Changelogs:
+- [dbt-core 1.9.1](https://github.com/dbt-labs/dbt-core/blob/1.9.latest/CHANGELOG.md#dbt-core-191---december-16-2024)
+- [dbt-adapters 1.13.1](https://github.com/dbt-labs/dbt-adapters/blob/main/CHANGELOG.md#dbt-adapters-1131---january-10-2025)
+- [dbt-common 1.14.0](https://github.com/dbt-labs/dbt-common/blob/main/CHANGELOG.md)
+- [dbt-bigquery 1.9.1](https://github.com/dbt-labs/dbt-bigquery/blob/1.9.latest/CHANGELOG.md#dbt-bigquery-191---january-10-2025)
+- [dbt-databricks 1.9.1](https://github.com/databricks/dbt-databricks/blob/main/CHANGELOG.md#dbt-databricks-191-december-16-2024)
+- [dbt-fabric 1.9.0](https://github.com/microsoft/dbt-fabric/releases/tag/v1.9.0)
+- [dbt-postgres 1.9.0](https://github.com/dbt-labs/dbt-postgres/blob/main/CHANGELOG.md#dbt-postgres-190---december-09-2024)
+- [dbt-redshift 1.9.0](https://github.com/dbt-labs/dbt-redshift/blob/1.9.latest/CHANGELOG.md#dbt-redshift-190---december-09-2024)
+- [dbt-snowflake 1.9.0](https://github.com/dbt-labs/dbt-snowflake/blob/1.9.latest/CHANGELOG.md#dbt-snowflake-190---december-09-2024)
+- [dbt-spark 1.9.0](https://github.com/dbt-labs/dbt-spark/blob/1.9.latest/CHANGELOG.md#dbt-spark-190---december-10-2024)
+- [dbt-synapse 1.8.2](https://github.com/microsoft/dbt-synapse/blob/v1.8.latest/CHANGELOG.md)
+- [dbt-teradata 1.9.0](https://github.com/Teradata/dbt-teradata/releases/tag/v1.9.0)
+- [dbt-trino 1.9.0](https://github.com/starburstdata/dbt-trino/blob/master/CHANGELOG.md#dbt-trino-190---december-20-2024)
+
 ## December 2024
 
 Release date: December 12, 2024

website/docs/docs/deploy/deploy-jobs.md

@@ -32,9 +32,9 @@ You can create a deploy job and configure it to run on [scheduled days and times
     - (Optional) **Description** — Provide a description of what the job does (for example, what the job consumes and what the job produces). 
     - **Environment** —  By default, it’s set to the deployment environment you created the deploy job from.
 3. Options in the **Execution settings** section:
-    - **Commands** — By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when the job runs.
-    - **Generate docs on run** — Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
-    - **Run source freshness** — Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
+    - [**Commands**](/docs/deploy/job-commands#built-in-commands) — By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when the job runs. During a job run, [built-in commands](/docs/deploy/job-commands#built-in-commands) are "chained" together and if one run step fails, the entire job fails with an "Error" status. 
+    - [**Generate docs on run**](/docs/deploy/job-commands#checkbox-commands) — Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs. If the step fails, the job can succeed if subsequent steps pass. 
+    - [**Run source freshness**](/docs/deploy/job-commands#checkbox-commands) — Enable this option to invoke the `dbt source freshness` command before running the deploy job. If the step fails, the job can succeed if subsequent steps pass. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Triggers** section:
     - **Run on schedule** — Run the deploy job on a set schedule.
         - **Timing** — Specify whether to [schedule](#schedule-days) the deploy job using **Intervals** that run the job every specified number of hours, **Specific hours** that run the job at specific times of day, or **Cron schedule** that run the job specified using [cron syntax](#cron-schedule).

website/docs/guides/customize-schema-alias.md

@@ -9,6 +9,7 @@ icon: 'guides'
 hide_table_of_contents: true
 level: 'Advanced'
 recently_updated: true
+keywords: ["generate", "schema name", "guide", "dbt", "schema customization", "custom schema"]
 ---
 
 <div style={{maxWidth: '900px'}}>

website/docs/reference/resource-configs/event-time.md

@@ -21,7 +21,6 @@ models:
 ```
 </File>
 
-
 <File name='models/properties.yml'>
 
 ```yml
@@ -139,20 +138,28 @@ sources:
 
 ## Definition
 
-Set the `event_time` to the name of the field that represents the timestamp of the event -- "at what time did the row occur" -- as opposed to an event ingestion date.  You can configure `event_time` for a [model](/docs/build/models), [seed](/docs/build/seeds), or [source](/docs/build/sources) in your `dbt_project.yml` file, property YAML file, or config block. 
+You can configure `event_time` for a [model](/docs/build/models), [seed](/docs/build/seeds), or [source](/docs/build/sources) in your `dbt_project.yml` file, property YAML file, or config block.
+
+`event_time` is required for the [incremental microbatch](/docs/build/incremental-microbatch) strategy and highly recommended for [Advanced CI's compare changes](/docs/deploy/advanced-ci#optimizing-comparisons) in CI/CD workflows, where it ensures the same time-slice of data is correctly compared between your CI and production environments.
+
+### Best practices
+
+Set the `event_time` to the name of the field that represents the actual timestamp of the event (like `account_created_at`). The timestamp of the event should represent "at what time did the row occur" rather than an event ingestion date. Marking a column as the `event_time` when it isn't, diverges from the semantic meaning of the column which may result in user confusion when other tools make use of the metadata.
 
-Here are some examples of good and bad `event_time` columns:
+However, if an ingestion date (like `loaded_at`, `ingested_at`, or `last_updated_at`) are the only timestamps you use, you can set `event_time` to these fields. Here are some considerations to keep in mind if you do this:
 
-- ✅ Good:
-  - `account_created_at` &mdash; This represents the specific time when an account was created, making it a fixed event in time.
-  - `session_began_at` &mdash; This captures the exact timestamp when a user session started, which won’t change and directly ties to the event.
+- Using `last_updated_at` or `loaded_at` &mdash; May result in duplicate entries in the resulting table in the data warehouse over multiple runs. Setting an appropriate [lookback](/reference/resource-configs/lookback) value can reduce duplicates but it can't fully eliminate them since some updates outside the lookback window won't be processed.
+- Using `ingested_at` &mdash; Since this column is created by your ingestion/EL tool instead of coming from the original source, it will change if/when you need to resync your connector for some reason. This means that data will be reprocessed and loaded into your warehouse for a second time against a second date. As long as this never happens (or you run a full refresh when it does), microbatches will be processed correctly when using `ingested_at`. 
 
-- ❌ Bad:
+Here are some examples of recommended and not recommended `event_time` columns:
 
-  - `_fivetran_synced` &mdash; This isn't the time that the event happened, it's the time that the event was ingested.
-  - `last_updated_at` &mdash; This isn't a good use case as this will keep changing over time. 
 
-`event_time` is required for [Incremental microbatch](/docs/build/incremental-microbatch) and highly recommended for [Advanced CI's compare changes](/docs/deploy/advanced-ci#optimizing-comparisons) in CI/CD workflows, where it ensures the same time-slice of data is correctly compared between your CI and production environments.
+| <div style={{width:'200px'}}>Status</div>      | Column name     | Description    |
+|--------------------|---------------------|----------------------|
+| ✅ Recommended | `account_created_at` | Represents the specific time when an account was created, making it a fixed event in time.                       |
+| ✅ Recommended | `session_began_at`    | Captures the exact timestamp when a user session started, which won’t change and directly ties to the event.     |
+| ❌ Not recommended | `_fivetran_synced`    | This represents the time the event was ingested, not when it happened.                                           |
+| ❌ Not recommended | `last_updated_at`    | Changes over time and isn't tied to the event itself. If used, note the considerations mentioned earlier in [best practices](#best-practices).    |
 
 ## Examples
 

website/docs/reference/resource-configs/snowflake-configs.md

@@ -337,6 +337,15 @@ For dbt limitations, these dbt features are not supported:
 - [Model contracts](/docs/collaborate/govern/model-contracts)
 - [Copy grants configuration](/reference/resource-configs/snowflake-configs#copying-grants)
 
+### Troubleshooting dynamic tables
+
+If your dynamic table model fails to rerun with the following error message after the initial execution:
+
+```sql
+SnowflakeDynamicTableConfig.__init__() missing 6 required positional arguments: 'name', 'schema_name', 'database_name', 'query', 'target_lag', and 'snowflake_warehouse'
+```
+Ensure that `QUOTED_IDENTIFIERS_IGNORE_CASE` on your account is set to `FALSE`. 
+
 ## Temporary tables
 
 Incremental table merges for Snowflake prefer to utilize a `view` rather than a `temporary table`. The reasoning is to avoid the database write step that a temporary table would initiate and save compile time. 

website/docs/reference/resource-configs/tags.md

@@ -93,6 +93,21 @@ resource_type:
 ```
 
 </File>
+
+To apply tags to a model in your `models/` directory, add the `config` property similar to the following example:
+
+<File name='models/model.yml'>
+
+```yaml
+models:
+  - name: my_model
+    description: A model description
+    config:
+      tags: ['example_tag']
+```
+
+</File>
+
 </TabItem>
 
 <TabItem value="config">
@@ -126,10 +141,24 @@ You can use the [`+` operator](/reference/node-selection/graph-operators#the-plu
 - `dbt run --select +model_name+` &mdash; Run a model, its upstream dependencies, and its downstream dependencies.
 - `dbt run --select tag:my_tag+ --exclude tag:exclude_tag` &mdash; Run model tagged with `my_tag` and their downstream dependencies, and exclude models tagged with `exclude_tag`, regardless of their dependencies.
 
+
+:::tip Usage notes about tags
+
+When using tags, consider the following: 
+
+- Tags are additive across project hierarchy.
+- Some resource types (like sources, exposures) require tags at the top level.
+
+Refer to [usage notes](#usage-notes) for more information.
+:::
+
 ## Examples
+
+The following examples show how to apply tags to resources in your project. You can configure tags in the `dbt_project.yml`, `schema.yml`, or SQL files.
+
 ### Use tags to run parts of your project
 
-Apply tags in your `dbt_project.yml` as a single value or a string:
+Apply tags in your `dbt_project.yml` as a single value or a string. In the following example, one of the models, the `jaffle_shop` model, is tagged with `contains_pii`. 
 
 <File name='dbt_project.yml'>
 
@@ -153,16 +182,52 @@ models:
         - "published"
 
 ```
+</File>
+
+
+### Apply tags to models
+
+This section demonstrates applying tags to models in the `dbt_project.yml`, `schema.yml`, and SQL files. 
+
+To apply tags to a model in your `dbt_project.yml` file, you would add the following:
+
+<File name='dbt_project.yml'>
+
+```yaml
+models:
+  jaffle_shop:
+    +tags: finance # jaffle_shop model is tagged with 'finance'.
+```
+
+</File>
+
+To apply tags to a model in your `models/` directory YAML file, you would add the following using the `config` property:
+
+<File name='models/stg_customers.yml'>
+
+```yaml
+models:
+  - name: stg_customers
+    description: Customer data with basic cleaning and transformation applied, one row per customer.
+    config:
+      tags: ['santi'] # stg_customers.yml model is tagged with 'santi'.
+    columns:
+      - name: customer_id
+        description: The unique key for each customer.
+        data_tests:
+          - not_null
+          - unique
+```
 
 </File>
 
-You can also apply tags to individual resources using a config block:
+To apply tags to a model in your SQL file, you would add the following:
 
 <File name='models/staging/stg_payments.sql'>
 
 ```sql
 {{ config(
-    tags=["finance"]
+    tags=["finance"] # stg_payments.sql model is tagged with 'finance'.
 ) }}
 
 select ...
@@ -211,14 +276,10 @@ seeds:
 
 <VersionBlock lastVersion="1.8">
 
-:::tip Upgrade to dbt Core 1.9
-
-Applying tags to saved queries is only available in dbt Core versions 1.9 and later.
-:::
+<VersionCallout version="1.9" />
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.9">
 
 This following example shows how to apply a tag to a saved query in the `dbt_project.yml` file. The saved query is then tagged with `order_metrics`.
 
@@ -263,7 +324,6 @@ Run resources with multiple tags using the following commands:
 # Run all resources tagged "order_metrics" and "hourly"
   dbt build --select tag:order_metrics tag:hourly
 ```
-</VersionBlock>
 
 ## Usage notes