Salesforce Data Cloud Quickstart

website/docs/docs/get-started-dbt.md

@@ -76,6 +76,12 @@ Learn more about [<Constant name="cloud" /> features](/docs/cloud/about-cloud/d
     link="https://docs.getdbt.com/guides/teradata"
     icon="teradata"/>
 
+<Card
+    title="Quickstart for dbt and Salesforce Data Cloud"
+    body="Use dbt with Salesforce Data Cloud for your data transformation workflows."
+    link="https://docs.getdbt.com/guides/salesforce"
+    icon="TODO"/> 
+
 </div>
 
 ## dbt local installations

website/docs/guides/salesforce-qs.md

@@ -0,0 +1,178 @@
+---
+title: "Quickstart for dbt and Salesforce Data Cloud"
+id: "salesforce-data-cloud"
+level: 'Beginner'
+icon: 'salesforce'
+hide_table_of_contents: true
+tags: ['Salesforce', 'Data Cloud', 'platform','Quickstart']
+---
+
+<div style={{maxWidth: '900px'}}>
+
+## Introduction
+
+In this quickstart guide, you'll learn how to use <Constant name="cloud" /> with **Salesforce Data Cloud** using the new dbt Salesforce Data Cloud adapter (powered by dbt Fusion). You'll learn how to:
+
+- Install Fusion and the Salesforce adapter
+- Connect <Constant name="cloud" /> or your local dbt environment to Salesforce Data Cloud
+- Run and test dbt models using Data Cloud objects
+- Understand key limitations and Data Cloud–specific model requirements
+
+:::tip Note
+This adapter is currently in **Alpha**. It is not production-ready and should only be used in sandbox or test environments. Features, commands, and workflows are subject to change.
+:::
+
+### Prerequisites​
+
+- You have a [<Constant name="cloud" /> account](https://www.getdbt.com/signup/) or a local dbt Fusion setup.  
+- You have access to a **Salesforce Data Cloud instance** with API access and credentials (OAuth and service token).  
+- You have a code editor such as **VS Code** (or [Cursor](https://cursor.sh/)) with permissions to install extensions.  
+- You have a connected Git provider with repository access.  
+
+You’ll need the following Salesforce credentials:
+- **Client ID (Consumer Key)** from your connected app secrets  
+- **Private Key Path** (the full file path to your downloaded `server.key`)  
+- **Salesforce username**
+
+### Related content
+
+- Learn more with [dbt Learn courses](https://learn.getdbt.com)
+- [CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
+- [Job notifications](/docs/deploy/job-notifications)
+- [Source freshness](/docs/deploy/source-freshness)
+
+## Install Fusion
+
+The Salesforce adapter is available through **dbt Fusion**, a next-generation dbt binary. Follow the installation steps for your platform.
+
+### macOS & Linux
+```bash
+curl -fsSL https://public.cdn.getdbt.com/fs/install/install.sh | sh -s -- --update
+exec $SHELL
+```
+### Windows (Powershell)
+```bash
+irm https://public.cdn.getdbt.com/fs/install/install.ps1 | iex
+Start-Process powershell
+```
+
+To verify Fusion installation run
+```bash
+dbtf --version
+```
+Installing Fusion automatically provides the Salesforce adapter and its ADBC driver.
+
+## Install the dbt Extension for VS Code
+
+1. In VS Code, navigate to the Extensions tab.
+2. Search for dbt and locate the extension published by dbtLabsInc or dbt Labs Inc.
+3. Click Install.
+4. Once installed, look for the dbt Extension label in your VS Code status bar.
+- Hover over it to see diagnostic info.
+- Register and activate the extension.
+
+After activation, the extension will automatically download the correct dbt Language Server for your OS.
+You can learn more about how to use the dbt VS Code extension in the [dbt Extension documentation](https://docs.getdbt.com/docs/install-dbt-extension)
+.
+
+## Set up a dbt Project
+
+1. Upload the provided dbt Salesforce Data Cloud example project (zip file) to your Git repository.
+2. Clone the repository locally through VS Code.
+3. Follow the instructions in the project’s README, starting from Step 3:
+- Create a profiles.yml
+- Confirm your connection
+- Compile and execute the sample Jaffle Shop dbt project
+- Run data tests to validate your models
+
+You should now be able to run dbtf run and dbtf test successfully against your Salesforce Data Cloud instance.
+
+## Connection Configuration
+Your profiles.yml file will look similar to the following:
+```yaml
+salesforce_data_cloud:
+  target: dev
+  outputs:
+    dev:
+      type: salesforce_data_cloud
+      method: oauth
+      client_id: "<YOUR_CLIENT_ID>"
+      private_key_path: "<YOUR_PATH>/server.key"
+      user: "<YOUR_USERNAME>"
+      database: "<YOUR_DATA_SPACE>"
+      schema: "default"
+      threads: 4
+```
+Then run:
+```bash
+dbtf debug
+```
+If successful, you should see:
+```sql
+Connection test: OK connection
+```
+
+## Execute Your First dbt Run
+1. Open your cloned dbt Salesforce project in VS Code.
+2. Run the following command in the terminal:
+```bash
+dbtf run --static-analysis off
+```
+This disables static analysis (required due to current Data Cloud limitations).
+If successful, your models will be built in Salesforce Data Cloud as Data Lake Objects (DLOs).
+
+## Known Limitations
+| Feature | Timeline | Notes |
+|----------|-----------|-------|
+| **Running `dbt run` twice** | Coming soon | Due to Data Cloud’s architecture, rerunning the same model is not currently supported. Manually delete dependencies before rerunning. |
+| **Materializations supported** | — | Only `table` materializations are supported. Incremental, snapshot, view, and ephemeral are not. |
+| **Static Analysis** | Ongoing | Must include `--static-analysis off` in every dbt command. Impacts column-level lineage and VS Code dbt buttons. |
+| **dbt Seeds** | Coming soon | Not yet supported. |
+| **dbt Docs** | N/A | Not currently available in Fusion for Data Cloud. |
+| **Arbitrary Queries** | Not on roadmap | All queries must be tied to defined dbt sources. |
+| **`SELECT *`** | Coming | Standard metadata queries currently fail due to injected system columns. |
+| **Multi-dataspace writes** | Coming soon | Future support for writing to multiple dataspaces. |
+| **Configurable timeout** | Coming soon | Defaults to 5 minutes; not configurable yet. |
+| **Canceling dbt runs** | Coming soon | Future support planned. |
+
+
+## VS Code Callouts
+- The Problems table in VS Code may show namespace errors due to Data Cloud’s lack of schema support.
+- Models must end with __dll. If omitted, it is appended automatically (for example, model_name → model_name__dll).
+- Columns must end with __c. Omitting this suffix causes syntax errors.
+- Model names cannot include double underscores (__) except before __dll.
+- For category=profile, all models must include:
+```yaml
+config:
+  category: profile
+  primary_key: id
+```
+
+## Example: Simple Model in Salesforce Data Cloud
+Create file in models folder
+```sql
+select
+    id__c as customer_id__c,
+    first_name__c,
+    last_name__c
+from {{ source('jaffle_shop', 'customers') }}
+```
+Run the model:
+```bash
+dbtf run --models customers__dll --static-analysis off
+```
+You should see the Data Lake Object created successfully in your Salesforce Data Cloud environment.
+
+#### FAQs {#faq-2}
+
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Project/unique-resource-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+
+</div>
+
+<Snippet path="quickstarts/test-and-document-your-project" />
+
+<Snippet path="quickstarts/schedule-a-job" />
+