[docs] small fixes (#682)

docs/website/docs/dlt-ecosystem/verified-sources/airtable.md

@@ -6,7 +6,7 @@ keywords: [airtable api, airtable verified source, airtable]
 
 # Airtable
 
-Airtable is a cloud-based platform that merges spreadsheet and database functionalities for easy
+[Airtable](https://www.airtable.com/) is a cloud-based platform that merges spreadsheet and database functionalities for easy
 data management and collaboration.
 
 This Airtable `dlt` verified source and
@@ -24,8 +24,6 @@ Sources and resources that can be loaded using this verified source are:
 
 ### Grab Airtable personal access tokens
 
-
-
 1. Click your account icon top-right.
 1. Choose "Developer Hub" from the dropdown.
 1. Select "Personal access token" on the left, then "Create new token".
@@ -106,6 +104,8 @@ For more information, read the
    > Optionally, you can also input "base_id" and "table_names" in the script, as in the pipeline
    > example.
 
+For more information, read the [General Usage: Credentials.](../../general-usage/credentials)
+
 ## Run the pipeline
 
 1. Before running the pipeline, ensure that you have installed all the necessary dependencies by
@@ -118,7 +118,7 @@ For more information, read the
 1. You're now ready to run the pipeline! To get started, run the following command:
 
    ```bash
-   python3 airtable_pipeline.py
+   python airtable_pipeline.py
    ```
 
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using

docs/website/docs/dlt-ecosystem/verified-sources/asana.md

@@ -12,7 +12,7 @@ keywords: [asana api, verified source, asana]
 or [book a call](https://calendar.app.google/kiLhuMsWKpZUpfho6) with our support engineer Adrian.
 :::
 
-Asana is a widely used web-based project management and collaboration tool that helps teams stay
+[Asana](https://asana.com) is a widely used web-based project management and collaboration tool that helps teams stay
 organized, focused, and productive. With Asana, team members can easily create, assign, and track
 tasks, set deadlines, and communicate with each other in real-time.
 
@@ -47,8 +47,8 @@ To get a complete list of sub-endpoints that can be loaded, see
 1. This token will be used to configure `.dlt/secrets.toml`, so keep it secure and don't share it
    with anyone.
 
-More information you can see in the
-[Asana official documentation](https://developers.asana.com/docs/authentication).
+> Note: The Asana UI, which is described here, might change.
+The full guide is available at [this link.](https://developers.asana.com/docs/authentication)
 
 ### Initialize the verified source
 
@@ -100,7 +100,7 @@ For more information, read the [General Usage: Credentials.](../../general-usage
    ```
 1. You're now ready to run the pipeline! To get started, run the following command:
    ```bash
-   python3 asana_dlt_pipeline.py
+   python asana_dlt_pipeline.py
    ```
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using
    the following command:

docs/website/docs/dlt-ecosystem/verified-sources/chess.md

@@ -74,7 +74,7 @@ For more information, read the [General Usage: Credentials.](../../general-usage
 1. You're now ready to run the pipeline! To get started, run the following command:
 
    ```bash
-   python3 chess_pipeline.py
+   python chess_pipeline.py
    ```
 
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using

docs/website/docs/dlt-ecosystem/verified-sources/facebook_ads.md

@@ -18,7 +18,7 @@ Facebook and its affiliated apps like Instagram and Messenger.
 
 This Facebook `dlt` verified source and
 [pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/facebook_ads_pipeline.py)
-loads data using “Facebook Marketing API” to the destination of your choice.
+loads data using [Facebook Marketing API](https://developers.facebook.com/products/marketing-api/) to the destination of your choice.
 
 The endpoints that this verified source supports are:
 
@@ -85,13 +85,17 @@ debug_access_token()
 
 We highly recommend you to add the token expiration timestamp to get notified a week before token
 expiration that you need to rotate it. Right now the notifications are sent to logger with error
-level. In config.toml / secrets.toml:
+level. In `config.toml` / `secrets.toml`:
 
 ```toml
 [sources.facebook_ads]
-access_token_expires_at=1688821881
+access_token_expires_at=1688821881...
 ```
 
+> Note: The Facebook UI, which is described here, might change.
+The full guide is available at [this link.](https://developers.facebook.com/docs/marketing-apis/overview/authentication)
+
+
 ### Initialize the verified source
 
 To get started with your data pipeline, follow these steps:
@@ -150,6 +154,8 @@ For more information, read the
 
 1. Replace the value of the "account id" with the one [copied above](#grab-account-id).
 
+For more information, read the [General Usage: Credentials.](../../general-usage/credentials)
+
 ## Run the pipeline
 
 1. Before running the pipeline, ensure that you have installed all the necessary dependencies by
@@ -159,7 +165,7 @@ For more information, read the
    ```
 1. You're now ready to run the pipeline! To get started, run the following command:
    ```bash
-   python3 facebook_ads_pipeline.py
+   python facebook_ads_pipeline.py
    ```
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using
    the following command:
@@ -269,7 +275,7 @@ def facebook_insights_source(
 ) -> DltResource:
 ```
 
-`account_id`: Account id associated with add manager, configured in _config.toml_.
+`account_id`: Account id associated with ads manager, configured in _config.toml_.
 
 `access_token`: Access token associated with the Business Facebook App, configured in
 _secrets.toml_.

docs/website/docs/dlt-ecosystem/verified-sources/github.md

@@ -13,7 +13,7 @@ or [book a call](https://calendar.app.google/kiLhuMsWKpZUpfho6) with our support
 :::
 
 This verified source can be used to load data on issues or pull requests from any GitHub repository
-onto a [destination](../../dlt-ecosystem/destinations) of your choice.
+onto a [destination](../../dlt-ecosystem/destinations) of your choice using [GitHub API](https://docs.github.com/en/rest?apiVersion=2022-11-28).
 
 Resources that can be loaded using this verified source are:
 
@@ -52,14 +52,13 @@ To get the API token, sign-in to your GitHub account and follow these steps:
 
 1. Copy the token and save it. This is to be added later in the `dlt` configuration.
 
-> You can optionally add API access tokens to avoid making requests as an unauthorized user.\
-> Note:
+> You can optionally add API access tokens to avoid making requests as an unauthorized user.
 > If you wish to load data using the github_reaction source, the access token is mandatory.
 
 More information you can see in the
 [GitHub authentication](https://docs.github.com/en/rest/overview/authenticating-to-the-rest-api?apiVersion=2022-11-28#basic-authentication)
 and
-[Github API token scopes](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps)
+[GitHub API token scopes](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps)
 documentations.
 
 ### Initialize the verified source
@@ -106,7 +105,7 @@ For more information, read the
    add credentials for your chosen destination, ensuring proper routing of your data to the final
    destination.
 
-For more information, read the [Walkthrough: Run a pipeline.](../../walkthroughs/run-a-pipeline)
+For more information, read the [General Usage: Credentials.](../../general-usage/credentials)
 
 ## Run the pipeline
 
@@ -117,7 +116,7 @@ For more information, read the [Walkthrough: Run a pipeline.](../../walkthroughs
    ```
 1. You're now ready to run the pipeline! To get started, run the following command:
    ```bash
-   python3 github_pipeline.py
+   python github_pipeline.py
    ```
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using
    the following command:
@@ -127,6 +126,8 @@ For more information, read the [Walkthrough: Run a pipeline.](../../walkthroughs
    For example, the `pipeline_name` for the above pipeline example is `github_reactions`, you may
    also use any custom name instead.
 
+For more information, read the [Walkthrough: Run a pipeline.](../../walkthroughs/run-a-pipeline)
+
 ## Sources and resources
 
 `dlt` works on the principle of [sources](../../general-usage/source) and

docs/website/docs/dlt-ecosystem/verified-sources/google_analytics.md

@@ -36,18 +36,18 @@ tokens are preferred when user consent is required, while service account creden
 suited for server-to-server interactions. You can choose the method of authentication as per your
 requirement.
 
-### Grab google service account credentials
+### Grab Google service account credentials
 
-You need to create a GCP service account to get API credentials, if you don't have one. To create
-one follow these steps:
+You need to create a GCP service account to get API credentials if you don't have one. To create
+ one, follow these steps:
 
 1. Sign in to [console.cloud.google.com](http://console.cloud.google.com/).
 
-1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#creating) if
+1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#creating) if
    needed.
 
 1. Enable "Google Analytics API", refer
-   [google documentation](https://support.google.com/googleapi/answer/6158841?hl=en) for
+   [Google documentation](https://support.google.com/googleapi/answer/6158841?hl=en) for
    comprehensive instructions on this process.
 
 1. Generate credentials:
@@ -60,7 +60,7 @@ one follow these steps:
 
 ### Grab google OAuth credentials
 
-You need to create a GCP account to get OAuth credentials, if you don't have one. To create one
+You need to create a GCP account to get OAuth credentials if you don't have one. To create one,
 follow these steps:
 
 1. Ensure your email used for the GCP account has access to the GA4 property.
@@ -91,10 +91,10 @@ follow these steps:
 1. Add your email as a test user.
 
 After configuring "client_id", "client_secret" and "project_id" in "secrets.toml". To generate the
-refresh token run the following script from the root folder:
+refresh token, run the following script from the root folder:
 
 ```bash
-python3 google_analytics/setup_script_gcp_oauth.py
+python google_analytics/setup_script_gcp_oauth.py
 ```
 
 Once you have executed the script and completed the authentication, you will receive a "refresh
@@ -161,10 +161,10 @@ For more information, read the
 
 1. From the ".json" that you
    [downloaded earlier](google_analytics.md#grab-google-service-account-credentials),
-   copy `project_id`, `private_key`,
-   and `client_email` under `[sources.google_analytics.credentials]`.
+   copy `project_id`, `private_key`,
+   and `client_email` under `[sources.google_analytics.credentials]`.
 
-1. Alternatively, if you're using OAuth credentials, replace the the fields and values with those
+1. Alternatively, if you're using OAuth credentials, replace the fields and values with those
    you [grabbed for OAuth credentials](google_analytics.md#grab-google-oauth-credentials).
 
 1. The secrets.toml for OAuth authentication looks like:
@@ -177,7 +177,7 @@ For more information, read the
    project_id = "project_id" # please set me up!
    ```
 
-1. Finally, enter credentials for your chosen destination as per the [docs](../destinations/).
+1. Finally, enter credentials for your chosen destination as per the [docs](../destinations/).
 
 #### Pass `property_id` and `request parameters`
 
@@ -209,6 +209,8 @@ For more information, read the
 1. To use queries from `.dlt/config.toml`, run the `simple_load_config()` function in
    [pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/google_analytics_pipeline.py).
 
+For more information, read the [General Usage: Credentials.](../../general-usage/credentials)
+
 ## Run the pipeline
 
 1. Before running the pipeline, ensure that you have installed all the necessary dependencies by
@@ -218,7 +220,7 @@ For more information, read the
    ```
 1. You're now ready to run the pipeline! To get started, run the following command:
    ```bash
-   python3 google_analytics_pipeline.py
+   python google_analytics_pipeline.py
    ```
 1. Once the pipeline has finished running, you can verify that everything loaded correctly by using
    the following command:
@@ -295,6 +297,7 @@ def metrics_table(metadata: Metadata) -> Iterator[TDataItem]:
 Similarly, there is a transformer function called `dimensions_table` that populates table called
 "dimensions" with the data from each dimension.
 
+## Customization
 ### Create your own pipeline
 
 If you wish to create your own pipelines, you can leverage source and resource methods from this
@@ -304,9 +307,9 @@ verified source.
 
    ```python
    pipeline = dlt.pipeline(
-      pipeline_name="google_analytics",  # Use a custom name if desired
-      destination="duckdb",  # Choose the appropriate destination (e.g., duckdb, redshift, post)
-      dataset_name="GA4_data"  # Use a custom name if desired
+       pipeline_name="google_analytics",  # Use a custom name if desired
+       destination="duckdb",  # Choose the appropriate destination (e.g., duckdb, redshift, post)
+       dataset_name="GA4_data"  # Use a custom name if desired
    )
    ```