Framework Setup

Fabric Workspace Setup

Here we will create a new dbt project and configure it to use the dbt-fabricsparknb adapter. But, before we do this we need to gather some information from the Power BI / Fabric Portal. To do this, follow the steps below:

1. Open the Power BI Portal and navigate to the workspace you want to use for development. If necessary, create a new workspace.
2. Ensure that the workspace is Fabric enabled. If not, enable it.
3. Make sure that there is at least one Datalake in the workspace.
4. Get the connection details for the workspace.
   1. Get the lakehouse name, the workspace id, and the lakehouse id.
   2. The lakehouse name and workspace name are easily viewed from the fabric / power bi portal.
   3. The easiest way to get this information is to
      1. Navigate to a file or folder in the lakehouse,
      2. click on the three dots to the right of the file or folder name, and select "Properties". Details will be displayed in the properties window.
      3. From these properties select copy url and paste it into a text editor. The workspace id is the first GUID in the URL, the lakehouse id is the second GUID in the URL.
      4. In the example below, the workspace id is 4f0cb887-047a-48a1-98c3-ebdb38c784c2 and the lakehouse id is aa2e5f92-53cc-4ab3-9a54-a6e5b1aeb9a9.

https://onelake.dfs.fabric.microsoft.com/4f0cb887-047a-48a1-98c3-ebdb38c784c2/aa2e5f92-53cc-4ab3-9a54-a6e5b1aeb9a9/Files/notebooks

Create Dbt Project

Once you have taken note of the workspace id, lakehouse id, and lakehouse name, you can create a new dbt project and configure it to use the dbt-fabricsparknb adapter. To do this, run the code shown below:

!> Important Note when asked to select the adapter choose dbt-fabricksparknb. If you can't see the adapter, first install the dbt-fabricsparknb package from repository. During this process you will also be asked for the workspace id, lakehouse id, and lakehouse name. Use the values you gathered from the Power BI Portal.

# Create your dbt project directories and profiles.yml file
dbt init my_project # Note that the name of the project is arbitrary... call it whatever you like

The command above will create a new directory called my_project. Within this directory you will find a dbt_project.yml file. Open this file in your favourite text editor and note that it should look like the example below except that in your case my_project will be replaced with the name of the project you created above.:

# Name your project! Project names should contain only lowercase characters
# and underscores. A good package name should reflect your organization's
# name or the intended use of these models
name: 'my_project'
version: '1.0.0'

# This setting configures which "profile" dbt uses for this project.
profile: 'my_project'

# These configurations specify where dbt should look for different types of files.
# The `model-paths` config, for example, states that models in this project can be
# found in the "models/" directory. You probably won't need to change these!
model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]

clean-targets:         # directories to be removed by `dbt clean`
  - "target"
  - "dbt_packages"


# Configuring models
# Full documentation: https://docs.getdbt.com/docs/configuring-models

# In this example config, we tell dbt to build all models in the example/
# directory as views. These settings can be overridden in the individual model
# files using the ` config(...) ` macro.
models:
  test4:
    # Config indicated by + and applies to all files under models/example/
    example:
      +materialized: view

The dbt init command will also update your profiles.yml file with a profile matching your dbt project name. Open this file in your favourite text editor using the command below:

WindowsMacOSLinux

code  $home/.dbt/profiles.yml

code  ~/.dbt/profiles.yml

code  ~/.dbt/profiles.yml

When run this will display a file similar to the one below. Check that your details are correct.

my_project:
  outputs:
    dev:
      auth: cli #remove
      client_id: dlkdjl #remove
      client_scrent: dlkdjl #remove
      connect_retries: 0 #remove
      connect_timeout: 0 #remove
      endpoint: dkld #remove
      lakehouse: 'lakehouse' #the name of your lakehouse
      lakehouseid: 'aa2e5f92-53cc-4ab3-9a54-a6e5b1aeb9a9' #the guid of your lakehouse
      log_lakehouse: 'loglakehouse' #the name of your logging lakehouse, this is not required as lakehouse will be used by default
      method: livy
      schema: dbo #the schema you want to use
      tenant_id: '72f988bf-86f1-41af-91ab-2d7cd011db47' #your power bi tenant id
      threads: 1 #the number of threads to use
      type: fabricsparknb #the type of adapter to use.. always use fabricsparknb
      workspaceid: '4f0cb887-047a-48a1-98c3-ebdb38c784c2' #the guid of your workspace
  target: dev

This concludes the Framework setup.

Info

You are now ready to move to the next step in which you will set up your dbt project. Follow the dbt Build Process guide.