From f8d196e5894dd5e55a7ca5fa097ca289c82fc898 Mon Sep 17 00:00:00 2001 From: Anks Su <6273915+anksu2024@users.noreply.github.com> Date: Wed, 18 Oct 2023 17:00:21 -0700 Subject: [PATCH 1/4] ensure learners don't try to create a new file in main file Updated instructions to ensure that the learners don't struggle with creating new files in the main branch. --- website/docs/quickstarts/bigquery-qs.md | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md index e50b8aff26a..18086e9cbfb 100644 --- a/website/docs/quickstarts/bigquery-qs.md +++ b/website/docs/quickstarts/bigquery-qs.md @@ -88,7 +88,7 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile 4. Click **Upload a Service Account JSON File** in settings. 5. Select the JSON file you downloaded in [Generate BigQuery credentials](#generate-bigquery-credentials) and dbt Cloud will fill in all the necessary fields. 6. Click **Test Connection**. This verifies that dbt Cloud can access your BigQuery account. -7. Click **Next** if the test succeeded. If it failed, you might need to go back and regenerate your BigQuery credentials. +7. Click **Next** if the test succeeded. If it fails, you might need to go back and regenerate your BigQuery credentials. ## Set up a dbt Cloud managed repository @@ -100,13 +100,10 @@ Now that you have a repository configured, you can initialize your project and s 1. Click **Start developing in the IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. 2. Above the file tree to the left, click **Initialize dbt project**. This builds out your folder structure with example models. -3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code. +3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add a new dbt code. 4. You can now directly query data from your warehouse and execute `dbt run`. You can try this out now: - - Click **+ Create new file**, add this query to the new file, and click **Save as** to save the new file: - ```sql - select * from `dbt-tutorial.jaffle_shop.customers` - ``` - In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message. + - To confirm the success of the above command, navigate to the BigQuery Console and discover the newly created models. ## Build your first model 1. Under **Version Control** on the left, click **Create branch**. You can name it `add-customers-model`. You need to create a new branch since the main branch is set to read-only mode. @@ -174,7 +171,7 @@ select * from final 6. Enter `dbt run` in the command prompt at the bottom of the screen. You should get a successful run and see the three models. -Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned up data rather than raw data in your BI tool. +Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned-up data rather than raw data in your BI tool. #### FAQs @@ -282,7 +279,7 @@ Later, you can connect your business intelligence (BI) tools to these views and 4. Execute `dbt run`. - This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders` and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies. + This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders`, and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies. #### FAQs {#faq-2} From ee04d8be879bf0064f33b35c3b5ff552398e2d35 Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Thu, 19 Oct 2023 19:29:52 -0400 Subject: [PATCH 2/4] Update website/docs/quickstarts/bigquery-qs.md --- website/docs/quickstarts/bigquery-qs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md index 18086e9cbfb..63076a334ec 100644 --- a/website/docs/quickstarts/bigquery-qs.md +++ b/website/docs/quickstarts/bigquery-qs.md @@ -88,7 +88,7 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile 4. Click **Upload a Service Account JSON File** in settings. 5. Select the JSON file you downloaded in [Generate BigQuery credentials](#generate-bigquery-credentials) and dbt Cloud will fill in all the necessary fields. 6. Click **Test Connection**. This verifies that dbt Cloud can access your BigQuery account. -7. Click **Next** if the test succeeded. If it fails, you might need to go back and regenerate your BigQuery credentials. +7. Click **Next** if the test succeeds. If it fails, you might need to go back and regenerate your BigQuery credentials. ## Set up a dbt Cloud managed repository From a24dc9c8994ae25fcfa39c60df76d0de8194ee20 Mon Sep 17 00:00:00 2001 From: Anks Su <6273915+anksu2024@users.noreply.github.com> Date: Mon, 23 Oct 2023 20:39:16 -0700 Subject: [PATCH 3/4] Update bigquery-qs.md Updated the heading to reflect what the section does. --- website/docs/quickstarts/bigquery-qs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md index 63076a334ec..93148f41f40 100644 --- a/website/docs/quickstarts/bigquery-qs.md +++ b/website/docs/quickstarts/bigquery-qs.md @@ -95,7 +95,7 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile -## Initialize your dbt project​ and start developing +## Initialize your dbt project Now that you have a repository configured, you can initialize your project and start development in dbt Cloud: 1. Click **Start developing in the IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. From 6e93a69ad1e9e4b557fd703f64032f913a6cbeeb Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Tue, 24 Oct 2023 15:58:12 -0400 Subject: [PATCH 4/4] Update website/docs/quickstarts/bigquery-qs.md --- website/docs/quickstarts/bigquery-qs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md index 93148f41f40..546b56c234c 100644 --- a/website/docs/quickstarts/bigquery-qs.md +++ b/website/docs/quickstarts/bigquery-qs.md @@ -100,7 +100,7 @@ Now that you have a repository configured, you can initialize your project and s 1. Click **Start developing in the IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. 2. Above the file tree to the left, click **Initialize dbt project**. This builds out your folder structure with example models. -3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add a new dbt code. +3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code. 4. You can now directly query data from your warehouse and execute `dbt run`. You can try this out now: - In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message. - To confirm the success of the above command, navigate to the BigQuery Console and discover the newly created models.