1. Create a Google Cloud Project: If you haven’t already, create a new project in the Google Cloud Console.
2. Enable APIs: Navigate to the API Library and enable the YouTube Data API v3 and BigQuery API for your project.

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1. Create Credentials: In the Google Cloud Console, go to the credentials page and create OAuth 2.0 client IDs to authenticate your application.
2. Download Credentials: Download the JSON file with your credentials.
3. Set Environment Variable: Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the JSON file you downloaded.

1. Install Google API Client Library: Use pip to install the Google API client library for Python.pip install --upgrade google-api-python-client

2. Authenticate and Build Service: Use the credentials to authenticate and build the YouTube Analytics service object.
   ‍from googleapiclient.discovery import build
from oauth2client.client import GoogleCredentials

credentials = GoogleCredentials.get_application_default()
youtubeAnalytics = build('youtubeAnalytics', 'v2', credentials=credentials)

3. Query YouTube Analytics API: Define the metrics, dimensions, and filters you need, and query the YouTube Analytics API to retrieve your data.
   ‍response = youtubeAnalytics.reports().query(
   ids='channel==MINE',
   startDate='2023-01-01',
   endDate='2023-01-31',
   metrics='views,likes,dislikes',
   dimensions='video',
   sort='video'
).execute()

4. Extract and Format Data: Extract the data from the response and format it as required for BigQuery, typically as a JSON or CSV file.

1. Create Schema: Define the schema for your BigQuery table that corresponds to the data extracted from YouTube Analytics.
2. Transform Data: Ensure the data types in your extracted data match the BigQuery schema.
3. Save Data: Save the transformed data to a Google Cloud Storage bucket as a JSON or CSV file.

1. Create BigQuery Dataset: In the BigQuery console, create a new dataset.
2. Create BigQuery Table: Create a new table in your dataset with the schema you defined earlier.
3. Load Data into BigQuery: Use the BigQuery command-line tool or the BigQuery API to load the data from Google Cloud Storage into your BigQuery table.
   bq load --source_format=CSV mydataset.mytable gs://mybucket/mydata.csv‍‍
4. Or using the BigQuery API in Python:
from google.cloud import bigquery

client = bigquery.Client()
dataset_id = 'my_dataset'
table_id = 'my_table'
job_config = bigquery.LoadJobConfig(
   source_format=bigquery.SourceFormat.CSV,
   skip_leading_rows=1,
   autodetect=True,
)

with open('path_to_my_data.csv', 'rb') as source_file:
   job = client.load_table_from_file(source_file, f'{dataset_id}.{table_id}', job_config=job_config)

job.result()  # Waits for the job to complete.
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5. Verify Data: Once the data is loaded, verify it in the BigQuery console to ensure accuracy.

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To automate the process, you can write a script that performs steps 3 to 5 and schedule it to run at regular intervals using a scheduler like cron or Google Cloud Scheduler.

After the data has been successfully transferred, you can clean up any temporary files or data that is no longer required.

Notes:

• Ensure you handle rate limits and quotas for the YouTube Analytics API.
• Make sure to manage data consistency and integrity during the transformation step.
• Always secure your credentials and access to both YouTube Analytics data and BigQuery.
• Test the entire process end-to-end with a small dataset before scaling up.