diff --git a/docs/src/pages/docs/Connecting to Databases/google-bigquery.mdx b/docs/src/pages/docs/Connecting to Databases/google-bigquery.mdx
index c6a8aa5749c..3e3fefcfef7 100644
--- a/docs/src/pages/docs/Connecting to Databases/google-bigquery.mdx	
+++ b/docs/src/pages/docs/Connecting to Databases/google-bigquery.mdx	
@@ -11,32 +11,21 @@ version: 1
 The recommended connector library for BigQuery is
 [pybigquery](https://github.com/mxmzdlv/pybigquery).
 
-The connection string for BigQuery looks like:
-
+### Install BigQuery Driver
+Follow the steps [here](/docs/databases/dockeradddrivers) about how to
+install new database drivers when setting up Superset locally via docker-compose.
 ```
-bigquery://{project_id}
+echo "pybigquery" >> ./docker/requirements-local.txt
 ```
-
-When adding a new BigQuery connection in Superset, you'll also need to add the GCP Service Account
+### Connecting to BigQuery
+When adding a new BigQuery connection in Superset, you'll need to add the GCP Service Account
 credentials file (as a JSON).
 
 1. Create your Service Account via the Google Cloud Platform control panel, provide it access to the
    appropriate BigQuery datasets, and download the JSON configuration file for the service account.
-
-2. n Superset, Add a JSON blob to the **Secure Extra** field in the database configuration form with
-   the following format:
-
+2. In Superset, you can either upload that JSON or add the JSON blob in the following format (this should be the content of your credential JSON file):
 ```
 {
-    "credentials_info": <contents of credentials JSON file>
-}
-```
-
-The resulting file should have this structure:
-
-```
-{
-    "credentials_info": {
         "type": "service_account",
         "project_id": "...",
         "private_key_id": "...",
@@ -48,10 +37,49 @@ The resulting file should have this structure:
         "auth_provider_x509_cert_url": "...",
         "client_x509_cert_url": "..."
     }
-}
-```
+ ```
+
+![CleanShot 2021-10-22 at 04 18 11](https://user-images.githubusercontent.com/52086618/138352958-a18ef9cb-8880-4ef1-88c1-452a9f1b8105.gif)
+
+
+3. Additionally, can connect via SQLAlchemy URI instead
+
+    The connection string for BigQuery looks like:
+
+    ```
+    bigquery://{project_id}
+    ```
+    Go to the **Advanced** tab, Add a JSON blob to the **Secure Extra** field in the database configuration form with
+   the following format:
+   ```
+   {
+   "credentials_info": <contents of credentials JSON file>
+   }
+   ```
+
+   The resulting file should have this structure:
+   ```
+   {
+    "credentials_info": {
+        "type": "service_account",
+        "project_id": "...",
+        "private_key_id": "...",
+        "private_key": "...",
+        "client_email": "...",
+        "client_id": "...",
+        "auth_uri": "...",
+        "token_uri": "...",
+        "auth_provider_x509_cert_url": "...",
+        "client_x509_cert_url": "..."
+        }
+    }
+    ```
 
 You should then be able to connect to your BigQuery datasets.
 
+![CleanShot 2021-10-22 at 04 47 08](https://user-images.githubusercontent.com/52086618/138354340-df57f477-d3e5-42d4-b032-d901c69d2213.gif)
+
+
+
 To be able to upload CSV or Excel files to BigQuery in Superset, you'll need to also add the
 [pandas_gbq](https://github.com/pydata/pandas-gbq) library.