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Quickstart for dbt and Salesforce Data Cloud
salesforce-data-cloud
Beginner
salesforce
true
Salesforce
Data Cloud
platform
Quickstart

Introduction

In this quickstart guide, you'll learn how to use 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 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 account 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) 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

Install Fusion

The Salesforce adapter is available through dbt Fusion, a next-generation dbt binary. Follow the installation steps for your platform.

macOS & Linux

curl -fsSL https://public.cdn.getdbt.com/fs/install/install.sh | sh -s -- --update
exec $SHELL

Windows (Powershell)

irm https://public.cdn.getdbt.com/fs/install/install.ps1 | iex
Start-Process powershell

To verify Fusion installation run

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 .

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:

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:

dbtf debug

If successful, you should see:

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:
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:
config:
  category: profile
  primary_key: id

Example: Simple Model in Salesforce Data Cloud

Create file in models folder

select
    id__c as customer_id__c,
    first_name__c,
    last_name__c
from {{ source('jaffle_shop', 'customers') }}

Run the model:

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}