Merge branch 'current' into mwong-sl-st

website/docs/best-practices/clone-incremental-models.md

@@ -0,0 +1,79 @@
+---
+title: "Clone incremental models as the first step of your CI job"
+id: "clone-incremental-models"
+description: Learn how to define clone incremental models as the first step of your CI job.
+displayText: Clone incremental models as the first step of your CI job
+hoverSnippet: Learn how to clone incremental models for CI jobs.
+---
+
+Before you begin, you must be aware of a few conditions:
+- `dbt clone` is only available with dbt version 1.6 and newer. Refer to our [upgrade guide](/docs/dbt-versions/upgrade-core-in-cloud) for help enabling newer versions in dbt Cloud
+- This strategy only works for warehouse that support zero copy cloning (otherwise `dbt clone` will just create pointer views).
+- Some teams may want to test that their incremental models run in both incremental mode and full-refresh mode.
+
+Imagine you've created a [Slim CI job](/docs/deploy/continuous-integration) in dbt Cloud and it is configured to: 
+
+- Defer to your production environment.
+- Run the command `dbt build --select state:modified+` to run and test all of the models you've modified and their downstream dependencies.
+- Trigger whenever a developer on your team opens a PR against the main branch.
+
+<Lightbox src="/img/best-practices/slim-ci-job.png" width="70%" title="Example of a slim CI job with the above configurations" />
+
+Now imagine your dbt project looks something like this in the DAG:
+
+<Lightbox src="/img/best-practices/dag-example.png" width="70%" title="Sample project DAG" />
+
+When you open a pull request (PR) that modifies `dim_wizards`, your CI job will kickoff and build _only the modified models and their downstream dependencies_ (in this case, `dim_wizards` and `fct_orders`) into a temporary schema that's unique to your PR. 
+
+This build mimics the behavior of what will happen once the PR is merged into the main branch. It ensures you're not introducing breaking changes, without needing to build your entire dbt project. 
+
+## What happens when one of the modified models (or one of their downstream dependencies) is an incremental model?
+
+Because your CI job is building modified models into a PR-specific schema, on the first execution of `dbt build --select state:modified+`, the modified incremental model will be built in its entirety _because it does not yet exist in the PR-specific schema_ and [is_incremental will be false](/docs/build/incremental-models#understanding-the-is_incremental-macro). You're running in `full-refresh` mode.
+
+This can be suboptimal because:
+- Typically incremental models are your largest datasets, so they take a long time to build in their entirety which can slow down development time and incur high warehouse costs.
+- There are situations where a `full-refresh` of the incremental model passes successfully in your CI job but an _incremental_ build of that same table in prod would fail when the PR is merged into main (think schema drift where [on_schema_change](/docs/build/incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`)
+
+You can alleviate these problems by zero copy cloning the relevant, pre-exisitng incremental models into your PR-specific schema as the first step of the CI job using the `dbt clone` command. This way, the incremental models already exist in the PR-specific schema when you first execute the command `dbt build --select state:modified+` so the `is_incremental` flag will be `true`. 
+
+You'll have two commands for your dbt Cloud CI check to execute:
+1. Clone all of the pre-existing incremental models that have been modified or are downstream of another model that has been modified: `dbt clone --select state:modified+,config.materialized:incremental,state:old`
+2. Build all of the models that have been modified and their downstream dependencies: `dbt build --select state:modified+`
+
+Because of your first clone step, the incremental models selected in your `dbt build` on the second step will run in incremental mode.
+
+<Lightbox src="/img/best-practices/clone-command.png" width="70%" title="Clone command in the CI config" />
+
+Your CI jobs will run faster, and you're more accurately mimicking the behavior of what will happen once the PR has been merged into main. 
+
+### Expansion on "think schema drift" where [on_schema_change](/docs/build/incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`" from above
+
+Imagine you have an incremental model `my_incremental_model` with the following config:
+
+```sql
+
+{{
+    config(
+        materialized='incremental',
+        unique_key='unique_id',
+        on_schema_change='fail'
+    )
+}}
+
+```
+
+Now, let’s say you open up a PR that adds a new column to `my_incremental_model`. In this case:
+- An incremental build will fail.
+- A `full-refresh` will succeed.
+
+If you have a daily production job that just executes `dbt build` without a `--full-refresh` flag, once the PR is merged into main and the job kicks off, you will get a failure. So the question is - what do you want to happen in CI?
+- Do you want to also get a failure in CI, so that you know that once this PR is merged into main you need to immediately execute a `dbt build --full-refresh --select my_incremental_model` in production in order to avoid a failure in prod? This will block your CI check from passing.
+- Do you want your CI check to succeed, because once you do run a `full-refresh` for this model in prod you will be in a successful state? This may lead unpleasant surprises if your production job is suddenly failing when you merge this PR into main if you don’t remember you need to execute a `dbt build --full-refresh --select my_incremental_model` in production.
+
+There’s probably no perfect solution here; it’s all just tradeoffs! Our preference would be to have the failing CI job and have to manually override the blocking branch protection rule so that there are no surprises and we can proactively run the appropriate command in production once the PR is merged. 
+
+### Expansion on "why `state:old`"
+
+For brand new incremental models, you want them to run in `full-refresh` mode in CI, because they will run in `full-refresh` mode in production when the PR is merged into `main`. They also don't exist yet in the production environment... they're brand new!
+If you don't specify this, you won't get an error just a “No relation found in state manifest for…”. So, it technically works without specifying `state:old` but adding `state:old` is more explicit and means it won't even try to clone the brand new incremental models.

website/docs/community/resources/getting-help.md

@@ -60,4 +60,4 @@ If you want to receive dbt training, check out our [dbt Learn](https://learn.get
 - Billing
 - Bug reports related to the web interface
 
-As a rule of thumb, if you are using dbt Cloud, but your problem is related to code within your dbt project, then please follow the above process rather than reaching out to support.
+As a rule of thumb, if you are using dbt Cloud, but your problem is related to code within your dbt project, then please follow the above process rather than reaching out to support. Refer to [dbt Cloud support](/docs/dbt-support) for more information.

website/docs/docs/build/dimensions.md

@@ -15,7 +15,8 @@ In a data platform, dimensions is part of a larger structure called a semantic m
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-All dimensions require a `name`, `type` and in some cases, an `expr` parameter. 
+All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same model.
+
 
 | Parameter | Description | Type |
 | --------- | ----------- | ---- |

website/docs/docs/build/entities.md

@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Entities are real-world concepts in a business such as customers, transactions, and ad campaigns. We often focus our analyses around specific entities, such as customer churn or annual recurring revenue modeling. We represent entities in our semantic models using id columns that serve as join keys to other semantic models in your semantic graph.
 
-Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter.
+Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same model.
 
 Entities can be specified with a single column or multiple columns. Entities (join keys) in a semantic model are identified by their name. Each entity name must be unique within a semantic model, but it doesn't have to be unique across different semantic models. 
 

website/docs/docs/build/materializations.md

@@ -14,6 +14,8 @@ pagination_next: "docs/build/incremental-models"
 - ephemeral
 - materialized view
 
+You can also configure [custom materializations](/guides/create-new-materializations?step=1) in dbt. Custom materializations are a powerful way to extend dbt's functionality to meet your specific needs. 
+
 
 ## Configuring materializations
 By default, dbt models are materialized as "views". Models can be configured with a different materialization by supplying the `materialized` configuration parameter as shown below.

website/docs/docs/build/measures.md

@@ -34,7 +34,8 @@ measures:
 
 When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. 
 
-Measure names must be **unique** across all semantic models in a project.
+Measure names must be unique across all semantic models in a project and can not be the same as an existing `entity` or `dimension` within that same model.
+
 
 ### Description
 

website/docs/docs/dbt-cloud-apis/sl-jdbc.md

@@ -352,6 +352,8 @@ semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 
 ## FAQs
 
+<FAQ path="Troubleshooting/sl-alpn-error" />
+
 - **Why do some dimensions use different syntax, like `metric_time` versus `[Dimension('metric_time')`?**<br />
 	When you select a dimension on its own, such as `metric_time` you can use the shorthand method which doesn't need the “Dimension” syntax. However, when you perform operations on the dimension, such as adding granularity, the object syntax `[Dimension('metric_time')` is required. 
 

website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

@@ -33,6 +33,7 @@ import AvailIntegrations from '/snippets/_sl-partner-links.md';
 - <span><a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate and query your metrics in downstream tools.
 - [dbt Semantic Layer API query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) 
 - [Hex dbt Semantic Layer cells](https://learn.hex.tech/docs/logic-cell-types/transform-cells/dbt-metrics-cells) to set up SQL cells in Hex.
+- [Resolve 'Failed APN'](/faqs/Troubleshooting/sl-alpn-error) error when connecting to the dbt Semantic Layer.
 
 </VersionBlock>
 

website/docs/docs/use-dbt-semantic-layer/gsheets.md

@@ -56,10 +56,9 @@ To use the filter functionality, choose the [dimension](docs/build/dimensions) y
    - For categorical dimensiosn, type in the dimension value you want to filter by (no quotes needed) and press enter. 
    - Continue adding additional filters as needed with AND and OR. If it's a time dimension, choose the operator and select from the calendar. 
 
-
-
 **Limited Use Policy Disclosure**
 
 The dbt Semantic Layer for Sheet's use and transfer to any other app of information received from Google APIs will adhere to [Google API Services User Data Policy](https://developers.google.com/terms/api-services-user-data-policy), including the Limited Use requirements.
 
-
+## FAQs
+<FAQ path="Troubleshooting/sl-alpn-error" />

website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

@@ -14,6 +14,8 @@ The dbt Semantic Layer allows you to define metrics and use various interfaces t
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="85%" title="The diagram displays how your data flows using the dbt Semantic Layer and the variety of integration tools it supports."/>
 
+
+
 ## dbt Semantic Layer components
 
 The dbt Semantic Layer includes the following components:

website/docs/docs/use-dbt-semantic-layer/tableau.md

@@ -37,7 +37,7 @@ This integration provides a live connection to the dbt Semantic Layer through Ta
 
 2. Install the [JDBC driver](/docs/dbt-cloud-apis/sl-jdbc) to the folder based on your operating system:
    - Windows: `C:\Program Files\Tableau\Drivers`
-   - Mac: `~/Library/Tableau/Drivers`
+   - Mac: `~/Library/Tableau/Drivers` or `/Library/JDBC` or `~/Library/JDBC`
    - Linux: ` /opt/tableau/tableau_driver/jdbc`
 3. Open Tableau Desktop or Tableau Server and find the **dbt Semantic Layer by dbt Labs** connector on the left-hand side. You may need to restart these applications for the connector to be available.
 4. Connect with your Host, Environment ID, and Service Token information dbt Cloud provides during [Semantic Layer configuration](/docs/use-dbt-semantic-layer/setup-sl#:~:text=After%20saving%20it%2C%20you%27ll%20be%20provided%20with%20the%20connection%20information%20that%20allows%20you%20to%20connect%20to%20downstream%20tools). 
@@ -81,3 +81,5 @@ The following Tableau features aren't supported at this time, however, the dbt S
 - Filtering on a Date Part time dimension for a Cumulative metric type
 - Changing your date dimension to use "Week Number"
 
+## FAQs
+<FAQ path="Troubleshooting/sl-alpn-error" />

website/docs/faqs/Troubleshooting/sl-alpn-error.md

@@ -0,0 +1,14 @@
+---
+title: I'm receiving an `Failed ALPN` error when trying to connect to the dbt Semantic Layer.
+description: "To resolve the 'Failed ALPN' error in the dbt Semantic Layer, create a SSL interception exception for the dbt Cloud domain."
+sidebar_label: 'Use SSL exception to resolve `Failed ALPN` error'
+---
+
+If you're receiving a `Failed ALPN` error when trying to connect the dbt Semantic Layer with the various [data integration tools](/docs/use-dbt-semantic-layer/avail-sl-integrations) (such as Tableau, DBeaver, Datagrip, ADBC, or JDBC), it typically happens when connecting from a computer behind a corporate VPN or Proxy (like Zscaler or Check Point). 
+
+The root cause is typically the proxy interfering with the TLS handshake as the dbt Semantic Layer uses gRPC/HTTP2 for connectivity. To resolve this:
+
+- If your proxy supports gRPC/HTTP2 but isn't configured to allow ALPN, adjust its settings accordingly to allow ALPN. Or create an exception for the dbt Cloud domain.
+- If your proxy does not support gRPC/HTTP2, add an SSL interception exception for the dbt Cloud domain in your proxy settings
+
+This should help in successfully establishing the connection without the Failed ALPN error.

website/docs/guides/create-new-materializations.md

@@ -7,7 +7,6 @@ hoverSnippet: Learn how to create your own materializations.
 # time_to_complete: '30 minutes' commenting out until we test
 icon: 'guides'
 hide_table_of_contents: true
-tags: ['dbt Core']
 level: 'Advanced'
 recently_updated: true
 ---

website/docs/guides/sl-migration.md

@@ -91,13 +91,11 @@ At this point, both the new semantic layer and the old semantic layer will be ru
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that used the legacy Semantic Layer. 
 
-### Migration guide for Hex
+### Migration guide for Hex 
 
-To learn more about integrating with Hex, check out their [documentation](https://learn.hex.tech/docs/connect-to-data/data-connections/dbt-integration#dbt-semantic-layer-integration) for more info. Additionally, refer to [dbt Semantic Layer cells](https://learn.hex.tech/docs/logic-cell-types/transform-cells/dbt-metrics-cells) to set up SQL cells in Hex.
+To learn more about integrating with Hex, check out their [documentation](https://learn.hex.tech/docs/connect-to-data/data-connections/dbt-integration#dbt-semantic-layer-integration) for more info. Additionally, refer to [dbt Semantic Layer cells](https://learn.hex.tech/docs/logic-cell-types/transform-cells/dbt-metrics-cells) to set up SQL cells in Hex. 
 
-1. Set up a new connection for the Semantic Layer for your account. Something to note is that your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
-
-<LoomVideo id="752e85aabfbf4fa585008a5598f3517a" />
+1. Set up a new connection for the dbt Semantic Layer for your account. Something to note is that your legacy connection will still work.
 
 2. Re-create the dashboards or reports that use the legacy dbt Semantic Layer. 
 

website/sidebars.js

@@ -1060,6 +1060,7 @@ const sidebarSettings = {
             "best-practices/materializations/materializations-guide-7-conclusion",
           ],
         },
+        "best-practices/clone-incremental-models",
         "best-practices/writing-custom-generic-tests",
         "best-practices/best-practice-workflows",
         "best-practices/dbt-unity-catalog-best-practices",