1. Go to the [Google Developers Console](https://console.developers.google.com/).
2. Create a new project or select an existing one.
3. Enable the Google Sheets API for your project.
4. Go to "Credentials" and create a new service account.
5. Download the JSON file with your service account's credentials.
6. Share your target Google Sheet with the email address provided in the service account JSON file.

1. Connect to your Redshift cluster using an SQL client or command-line tool.
2. Write the SQL query to retrieve the data you want to move to Google Sheets.
3. Execute the query and export the results to a CSV file.
  - This can be done using the `UNLOAD` command in Redshift, which allows you to export data directly to Amazon S3, and then you can download the file from S3.

1. Install Python on your machine if it's not already installed.
2. Install the `google-auth` and `google-auth-oauthlib` libraries to authenticate with the Google Sheets API.
3. Install the `google-api-python-client` library to interact with the Google Sheets API.
4. Install `pandas` for handling data.

```bash
  pip install --upgrade google-auth google-auth-oauthlib google-api-python-client pandas
  ```

1. Create a new Python script in your preferred editor.
2. Import the necessary libraries:

  ```python
  from google.oauth2.service_account import Credentials
  from googleapiclient.discovery import build
  import pandas as pd
  ```

3. Define the scope and load your service account credentials:

  ```python
  SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
  SERVICE_ACCOUNT_FILE = 'path/to/your/service-account.json'

  creds = Credentials.from_service_account_file(
      SERVICE_ACCOUNT_FILE,
      scopes=SCOPES
  )
  ```

4. Build the Sheets API service:

  ```python
  service = build('sheets', 'v4', credentials=creds)
  ```

5. Read the CSV file with the data extracted from Redshift:

  ```python
  data_frame = pd.read_csv('path/to/your/data.csv')
  data_to_import = data_frame.values.tolist()
  ```

6. Define the ID of your Google Sheet and the range where you want to insert the data:

  ```python
  SAMPLE_SPREADSHEET_ID = 'your_spreadsheet_id'
  SAMPLE_RANGE_NAME = 'Sheet1!A1'  # Adjust the range accordingly
  ```

7. Use the Sheets API to update the sheet with your data:

  ```python
  sheet = service.spreadsheets()
  request = sheet.values().update(spreadsheetId=SAMPLE_SPREADSHEET_ID,
                                  range=SAMPLE_RANGE_NAME,
                                  valueInputOption='RAW',
                                  body={'values': data_to_import})
  response = request.execute()
  ```

8. Run your script to transfer the data from the CSV file to your Google Sheet.

‍

1. Open the Google Sheet you shared with your service account.
2. Verify that the data from the CSV file has been correctly inserted into the sheet.

Notes
- Make sure the Google Sheet has the necessary columns and formatting set up before running the script.
- Handle any exceptions or errors in your Python script, especially for larger datasets where you might exceed the Google Sheets API usage limits.
- Always adhere to best practices for handling credentials and sensitive data.
- If you're dealing with very large datasets, consider batching the data into smaller chunks to avoid hitting the Google Sheets API limits.

By following these steps, you can manually move data from Amazon Redshift to Google Sheets without using third-party connectors or integrations.