Update lesson 7

docs/dagster-university/pages/dagster-dbt/lesson-7/4-creating-the-manifest-during-deployment.md

@@ -6,13 +6,13 @@ lesson: '7'
 
 # Creating the manifest during deployment
 
-To recap, our deployment failed in the last section because Dagster couldn’t find a dbt manifest file, which it needs to turn dbt models into Dagster assets. This is because we built this file by running `dbt parse` during local development. You ran this manually in Lesson 3 and improved the experience in Lesson 4. However, you'll also need to build your dbt manifest file during deployment, which will require a couple additional steps. We recommend adopting CI/CD to automate this process.
+To recap, our deployment failed in the last section because Dagster couldn’t find a dbt manifest file, which it needs to turn dbt models into Dagster assets. This is because we built this file by running `dbt parse` during local development. You ran this manually in Lesson 3 and improved the experience using `DbtProject`'s `prepare_if_dev` in Lesson 4. However, you'll also need to build your dbt manifest file during deployment, which will require a couple additional steps. We recommend adopting CI/CD to automate this process.
 
 Building your manifest for your production deployment will be needed for both open source and Dagster+ deployments. In this case, Dagster+’s out-of-the-box `deploy.yml` GitHub Action isn’t aware that you’re also trying to deploy a dbt project with Dagster.
 
-Since your CI/CD will be running in a fresh environment, you'll need to install dbt and run `dbt deps` before building your manifest with `dbt parse`.
+Since your CI/CD will be running in a fresh environment, you'll need to install dbt and other dependencies before building your manifest.
 
-To get our deployment working, we need to add a step to our GitHub Actions workflow that runs the dbt commands required to generate the `manifest.json`. Specifically, we need to run `dbt deps` and `dbt parse` in the dbt project, just like you did during local development.
+To get our deployment working, we need to add a step to our GitHub Actions workflow that runs the commands required to generate the `manifest.json`. Specifically, we need to run the `dbt project prepare-and-package` command, available in the `dagster_dbt` package discussed earlier.
 
 1. In your Dagster project, locate the `.github/workflows` directory.
 2. Open the `deploy.yml` file.
@@ -29,8 +29,14 @@ To get our deployment working, we need to add a step to our GitHub Actions workf
         dagster-dbt project prepare-and-package --file dagster_university/assets/dbt.py
       shell: bash
     ```
-    
-5. Save and commit the changes. Make sure to push them to the remote!
+
+The code above:
+
+1. Creates a step named `Prepare DBT project for deployement`
+2. Upgrades `pip`, the package installer for Python 
+3. Navigates inside the `project-repo` folder
+4. Upgrades the project dependencies
+5. Prepares the manifest file by running the `dagster-dbt project prepare-and-package` command, specifying the file in which the `DbtProject` object is located.
 
 Once the new step is pushed to the remote, GitHub will automatically try to run a new job using the updated workflow.
 

docs/dagster-university/pages/dagster-dbt/lesson-7/5-preparing-for-a-successful-run.md

@@ -61,22 +61,47 @@ Because we’re still using a DuckDB-backed database, our `type` will also be `d
 
 ---
 
+## Adding a prod target to DbtProject
+
+Next, we need to update the `DbtProject` object in `dagster_university/assets/dbt.py` to specify what profile to target. To optimize the developer experience, let’s use an environment variable to specify the profile to target.
+
+1. In the `.env` file, define an environment variable named `DBT_TARGET` and set it to `dev`:
+
+   ```python
+   DBT_TARGET=dev
+   ```
+
+2. Next, import the `os` module at the top of the `dbt.py` file so the environment variable is accessible:
+
+   ```python
+   import os
+   ```
+
+3. Finally, scroll to the initialization of the DbtProject object, and use the new environment variable to access the profile to target. This should be on or around line 11:
+
+```python
+dbt_project = DbtProject(
+   project_dir=Path(__file__).joinpath("..", "..", "..", "analytics").resolve(),
+   target=os.getenv("DBT_TARGET")
+)
+```
+
+---
+
 ## Adding a prod target to deploy.yml
 
-Next, we need to update the dbt commands in the `.github/workflows/deploy.yml` file to target the new `prod` profile. This will ensure that dbt uses the correct connection details when the GitHub Action runs as part of our Dagster+ deployment.
+After that, we need to update the dbt commands in the `.github/workflows/deploy.yml` file to target the new `prod` profile. This will ensure that dbt uses the correct connection details when the GitHub Action runs as part of our Dagster+ deployment.
 
-Open the file, scroll to the dbt step you added, and add `-- target prod` after the `dbt parse` command. This command should be on or around line 52:
+Open the file, scroll to the environment variable sections, and set an environment variable named `DBT_TARGET` to `prod`. This command should be on or around line 12:
 
 ```bash
-- name: Parse dbt project and package with Dagster project
-  if: steps.prerun.outputs.result == 'pex-deploy'
-  run: |
-    pip install pip --upgrade
-    pip install dbt-duckdb
-    cd project-repo/analytics
-    dbt deps
-    dbt parse --target prod    ## add this flag
-  shell: bash
+env:
+  DAGSTER_CLOUD_URL: ${{ secrets.DAGSTER_CLOUD_ORGANIZATION }}
+  DAGSTER_CLOUD_API_TOKEN: ${{ secrets.DAGSTER_CLOUD_API_TOKEN }}
+  ENABLE_FAST_DEPLOYS: 'true'
+  PYTHON_VERSION: '3.8'
+  DAGSTER_CLOUD_FILE: 'dagster_cloud.yaml'
+  DBT_TARGET: 'prod'
 ```
 
 Save and commit the file to git. Don’t forget to push to remote!
@@ -104,7 +129,12 @@ The following table contains the environment variables we need to create in Dags
 ---
 
 - `DAGSTER_ENVIRONMENT`
-- Set this to `prod`. This will be used by your dbt resource to decide which target to use.
+- Set this to `prod`. This will be used by your resources and constants.
+
+---
+
+- `DBT_TARGET`
+- Set this to `prod`. This will be used by your dbt project and dbt resource to decide which target to use.
 
 ---