Ensure you have the necessary tools installed on your local machine. You will need Python installed, along with libraries such as `requests` for API calls and `google-cloud-bigquery` for interaction with BigQuery. Additionally, ensure you have a Google Cloud account and have set up billing for BigQuery.

Sign up for an account at News API (https://newsapi.org/) and create an API key. This key will be used to authenticate your requests to the News API. Keep this key secure and do not share it publicly.

Log into your Google Cloud Console. Create a new project if you don't have one. Within this project, create a new BigQuery dataset where the News API data will be stored. Take note of your project ID and dataset name as they will be needed in subsequent steps.

Create a Python script that sends a GET request to the News API endpoint, passing your API key and any desired parameters (such as specific news sources, keywords, or date ranges). Use the `requests` library to handle the HTTP request and parse the JSON response to extract the data you need.

```python
import requests

def fetch_news(api_key, query, from_date, to_date):
url = f'https://newsapi.org/v2/everything?q={query}&from={from_date}&to={to_date}&apiKey={api_key}'
response = requests.get(url)
data = response.json()
return data['articles']
```

Transform the fetched data into a format suitable for BigQuery. This usually involves structuring the data as a list of dictionaries, where each dictionary represents a row. Ensure you have all necessary fields and handle any missing or malformed data.

```python
def prepare_data(articles):
prepared_data = []
for article in articles:
row = {
"title": article.get('title'),
"description": article.get('description'),
"url": article.get('url'),
"publishedAt": article.get('publishedAt')
}
prepared_data.append(row)
return prepared_data
```

Use the `google-cloud-bigquery` Python library to load the prepared data into your BigQuery dataset. First, authenticate your session using a service account key file, then use the `insert_rows_json` method to load data into a specified BigQuery table.

```python
from google.cloud import bigquery

def load_data_to_bigquery(project_id, dataset_id, table_id, rows_to_insert):
client = bigquery.Client(project=project_id)
table_ref = client.dataset(dataset_id).table(table_id)
errors = client.insert_rows_json(table_ref, rows_to_insert)
if errors:
print(f"Encountered errors while inserting rows: {errors}")
else:
print("Data loaded successfully.")

# Example usage:
# load_data_to_bigquery('your-project-id', 'your-dataset-id', 'your-table-id', prepared_data)
```

Automate the process by scheduling the script to run at regular intervals. You can use cron jobs on a Unix-based system or Task Scheduler on Windows to run your script daily, weekly, or at another frequency. This step ensures that your BigQuery dataset remains up-to-date with the latest news data.

By following these steps, you can effectively move data from News API to BigQuery without relying on third-party connectors or integrations.