Merge pull request #594 from segmentio/repo-sync

repo sync

Author: bot-docsteam

src/_data/catalog/destination_categories.yml

@@ -1,5 +1,5 @@
 # AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
-# destination categories last updated 2022-09-22 
+# destination categories last updated 2022-09-26 
 items:
 - display_name: A/B Testing
   slug: a-b-testing

src/_data/catalog/destinations.yml

@@ -1,5 +1,5 @@
 # AUTOGENERATED LIST OF CONNECTIONS THAT SUPPORT REGIONAL
-# Last updated 2022-09-22 
+# Last updated 2022-09-26 
 warehouses:
   - id: WcjBCzUGff
     display_name: Azure SQL Data Warehouse

src/_data/catalog/regional-supported.yml

@@ -1,5 +1,5 @@
 # AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
-# source cateogries last updated 2022-09-22 
+# source cateogries last updated 2022-09-26 
 items:
   - display_name: A/B Testing
     slug: a-b-testing

src/_data/catalog/source_categories.yml

@@ -1,5 +1,5 @@
 # AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
-# sources last updated 2022-09-22 
+# sources last updated 2022-09-26 
 items:
   - id: 8HWbgPTt3k
     display_name: .NET

src/_data/catalog/sources.yml

@@ -207,9 +207,9 @@ You can read the [full Profile API docs](/docs/profiles/profile-api/) to learn m
 You can download a copy of your trait by visiting the the computed trait overview page.
 ![Downloading a CSV file of computed traits in Segment](../images/trait_overview.png)
 Computed Trait CSVs are generated on demand. Before you can download the CSV, you will need to generate it. There are three different options for formatting:
-- **Unformatted:** Contains three columns. The first contains the user or account key, the second contains the trait value and the third is a JSON object containing the external IDs. Generating this CSV is by far the fastest of the three options. [Download example unformatted CSV](files/trait_csv_format_a.csv)
-- **Distinct columns for unique external IDs (with indexed columns for ID types with multiple values):** Contains the same first three columns as the unformatted CSV. Additional columns are added for each distinct external ID type. When a single row has more than one value for a given external ID type, for example a user with three email addresses, _additional columns with indexed headers are added_, (`email`, `email_1`, `email_2`). [Download example formatted CSV with indexed columns](files/trait_csv_format_b.csv)
-- **Distinct columns for unique external IDs (with additional rows for ID types with multiple values):** Contains the same first three columns as the unformatted CSV. Additional columns are added for each distinct external ID type. When a single row has more than one value for a given external ID type, for example a user with two email addresses, _additional rows are added with the first three columns repeated (user or account key, trait value and external IDs JSON)._ [Download example formatted CSV with additional rows](files/trait_csv_format_c.csv)
+- **Unformatted:** Contains three columns. The first contains the user or account key, the second contains the trait value and the third is a JSON object containing the external IDs. Generating this CSV is by far the fastest of the three options. [Download example unformatted CSV](/docs/engage/files/trait_csv_format_a.csv)
+- **Distinct columns for unique external IDs (with indexed columns for ID types with multiple values):** Contains the same first three columns as the unformatted CSV. Additional columns are added for each distinct external ID type. When a single row has more than one value for a given external ID type, for example a user with three email addresses, _additional columns with indexed headers are added_, (`email`, `email_1`, `email_2`). [Download example formatted CSV with indexed columns](/docs/engage/files/trait_csv_format_b.csv)
+- **Distinct columns for unique external IDs (with additional rows for ID types with multiple values):** Contains the same first three columns as the unformatted CSV. Additional columns are added for each distinct external ID type. When a single row has more than one value for a given external ID type, for example a user with two email addresses, _additional rows are added with the first three columns repeated (user or account key, trait value and external IDs JSON)._ [Download example formatted CSV with additional rows](/docs/engage/files/trait_csv_format_c.csv)
 <table>
     <tr>
         <td>![Handling large CSV file downloads](../images/large_trait_csv.png)</td>

src/engage/audiences/computed-traits.md

@@ -84,38 +84,24 @@ To connect BigQuery to Segment SQL Traits, follow these instructions to create a
 
 2. Click the drop down to the left of the search bar and select the project that you want to connect.
 
-   ![Select a project to connect from the drop down menu](images/bigquery_setup1.png)
-
    > **Note**: If you don't see the project you want in the menu, click the account switcher in the upper right corner, and verify that you're logged in to the right Google account for the project.
 
 3. Click the menu in the upper left and select **IAM & Admin**, then **Service accounts**.
 
-5. Click **Create Service Account**.
-
-   ![Click Create Service Account on the Service accounts screen](images/bigquery_setup2.png)
-
-6. Give the service account a name like `segment-sqltraits`.
-
-7. Under **Project Role**, add _only_ the `BigQuery Data Viewer` and `BigQuery Job User` roles.
+4. Click **Create Service Account**.
 
-   ![Select a project role](images/bigquery_setup3a.png)
+5. Give the service account a name like `segment-sqltraits`.
 
-   ![Add the BigQuery Data Viewer and BigQuery Job User roles](images/bigquery_setup3b.png)
+6. Under **Project Role**, add _only_ the `BigQuery Data Viewer` and `BigQuery Job User` roles.
 
    > IMPORTANT: Do not add any other roles to the service account. Adding other roles can prevent Segment from connecting to the account.
 
-6. Click **Create Key**.
+7. Click **Create Key**.
 
-   ![Click Create Key](images/bigquery_setup4.png)
-
-7. Select `JSON` and click **Create**.
-
-   ![Select Json and click Create](images/bigquery_setup5.png)
+8. Select `JSON` and click **Create**.
 
    A file with the key is saved to your computer. Save this; you'll need it to set up the warehouse source in the next step.
 
-   ![A file key saved to your computer](images/bigquery_setup6.png)
-
    You're now ready to create a new BigQuery warehouse source, upload the JSON key you just downloaded, and complete the BigQuery setup.
 
 ### Step 2. Add the warehouse as a Source
@@ -219,7 +205,6 @@ Yes, Segment limits request sizes to a maximum of 16kb. Records larger than this
 ### I'm getting a permissions error.
 
 You might encounter a `permission denied for schema` error, like the following:
-![An example of a permission denied for schema error](images/troubleshoot1.png)
 
 Segment usually displays this error because you're querying a schema and table that the current user cannot access. To check the table privileges for a specific grantee (user), view the credentials of the stored warehouse user.
 
@@ -236,13 +221,11 @@ Learn more about granting permissions using the following links:
 
 ### I'm seeing a maximum columns error.
 
-![An example of a maximum columns error](images/troubleshoot2.png)
-
 Segment supports returning only 25 columns. [Contact Segment](https://segment.com/help/contact/){:target="_blank"} with a description of your use case if you need access to more than 25 columns.
 
 ### I'm seeing a duplicate `user_id` error.
 
-![An example of a duplicate user_id error](images/troubleshoot3.png)
+![An example of a duplicate user_id error](../images/troubleshoot3.png)
 
 Each query row must correspond to a unique user. Segment displays this error if it detects multiple rows with the same `user_id`. Use a `distinct` or `group by` statement to ensure that each row has a unique user_id.