BigQuery

Connecting to BigQuery

For an overview of the connector, please refer to the following link.

• Connecting to DB/SaaS

• Dashboard
• Local Version

1

Create a Connector

Select the “Connectors” tab on the top page and press the “Create” button.

2

Enter BigQuery Credentials

BigQuery offers two authentication methods.

Connecting with Service Account

Select BigQuery (Service Account) and enter the credentials in the displayed form.

Field Name	Description	Required	Example
Credential	Service Account JSON	✅	{"project_id": "", ...}

For creating a service account, please refer to the official page.

• Create a Service Account

Connecting with OAuth

Select BigQuery (OAuth) and press the displayed sign-in button to authenticate with Google.

The following fields are also required.

Field Name	Description	Required	Example
Project ID	Project ID	✅	demo-project
Dataset	Dataset Name		demo_dataset
Location	Location		asia-northeast1

After entering the information, press the Create button to complete the creation. If there are any errors, please check the connection details again.

3

Connect to the Created Database

Once created successfully, you can retrieve the connector name from the list. The string next to the icon is connection_name. You can use this to connect to the database in your code.

Enter the created connector name in SQL or Python code to retrieve data.

SQL

{{
    config(
        connection="connection_name"
    )
}}

select * from table_name

1

Create a Connector

Select the “Connectors” tab on the top page and press the “Create” button.

2

Enter BigQuery Credentials

BigQuery offers two authentication methods.

Connecting with Service Account

Select BigQuery (Service Account) and enter the credentials in the displayed form.

Field Name	Description	Required	Example
Credential	Service Account JSON	✅	{"project_id": "", ...}

For creating a service account, please refer to the official page.

• Create a Service Account

Connecting with OAuth

Select BigQuery (OAuth) and press the displayed sign-in button to authenticate with Google.

The following fields are also required.

Field Name	Description	Required	Example
Project ID	Project ID	✅	demo-project
Dataset	Dataset Name		demo_dataset
Location	Location		asia-northeast1

After entering the information, press the Create button to complete the creation. If there are any errors, please check the connection details again.

3

Connect to the Created Database

Once created successfully, you can retrieve the connector name from the list. The string next to the icon is connection_name. You can use this to connect to the database in your code.

Enter the created connector name in SQL or Python code to retrieve data.

SQL

{{
    config(
        connection="connection_name"
    )
}}

select * from table_name

Currently, only the Service Account method can be created via command in the local version. For other methods, please use the dashboard or directly write to ~/.morph/connections.yml.

1

Run the morph init Command

Run the morph init command to save the DB connection information to ~/.morph/connections.yml.

Shell

morph init

If you have not installed the morph package, please install it with the following command before proceeding.

shell

pip install morph-data

A list of database types will be displayed in the interactive interface, select Snowflake (User/Password).

Shell

Select your database type:
1. PostgreSQL
2. MySQL
3. Redshift
4. SQLServer
5. Snowflake (User/Password)
6. BigQuery (Service Account)
Enter the number corresponding to your database type: 6

BigQuery (Service Account) selected.

Next, enter the credentials. slug is treated as the connector name, which you specify in SQL or Python.

Replace the following input examples with actual values that can connect.

shell

Create a slug for your connection: bigquery-connection
Enter your BigQuery project ID: demo-project
Enter your BigQuery keyfile path: ~/path_to_pem_file
Enter your BigQuery dataset name (Optional): demo_dataset
Enter your BigQuery location (Optional): asia-northeast1

Once the credentials are saved, the following message will be displayed.

shell

Successfully initialized! 🎉
You can edit your connection details in `path_to_connections.yml`

connections.yml is saved as follows. If other connectors are created, they will be added under connections.

shell

cat ~/.morph/connections.yml

connections:
  bigquery-connection:
    dataset: demo_dataset
    keyfile: ~/path_to_pem_file
    location: asia-northeast1
    method: bigquery_service_account
    project: demo-project
    type: bigquery

2

Retrieve Data Using the Connector in Code

Enter the created connector name in SQL or Python code to retrieve data.

SQL

{{
    config(
        connection="connection_name"
    )
}}

select * from table_name

When actually specifying the connector and executing the file, the priority is as follows. Also, please note that only connectors created in the dashboard can be used in the environment where morph deploy is performed.

1. Connectors listed in ~/.morph/connections.yml on the local machine
2. Connectors registered in the cloud

How to Write for OAuth

When using OAuth, directly edit ~/.morph/connections.yml.

Refreshing the access token is a dashboard feature, so the one described will always be used in the local environment.

OAuth Method

connections:
  bigquery-oauth-connection
    type: bigquery # Fixed
    method: bigquery_oauth # Fixed
    project: str
    refresh_token: str
    client_id: str
    client_secret: str
    redirect_uri: str
    dataset: str # Optional
    location: str # Optional
    access_token: str # Optional