How to load data from Google Search Console to MongoDB

First, you'll need access to the Google Search Console API. Go to the Google Cloud Console and create a new project. Enable the Search Console API for your project. Then, set up OAuth 2.0 credentials by creating a new set of credentials and downloading the JSON file. This file will allow your script to authenticate and access data from the Google Search Console.

Ensure you have Python installed on your machine, as it will be used to interact with the API. You will also need to install necessary Python libraries, such as `google-auth`, `google-auth-oauthlib`, `google-auth-httplib2`, and `google-api-python-client`, using pip. Additionally, install the `pymongo` library to interact with MongoDB. Run the following command in your terminal:

```bash
pip install google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client pymongo
```

Write a Python script to authenticate using the credentials JSON file. Use the OAuth2 flow to obtain access tokens. Here's a basic code snippet to get you started:

```python
from google.oauth2 import service_account
from googleapiclient.discovery import build

# Load credentials from JSON file
credentials = service_account.Credentials.from_service_account_file(
'path/to/your/credentials.json',
scopes=['https://www.googleapis.com/auth/webmasters.readonly']
)

# Build the service
service = build('webmasters', 'v3', credentials=credentials)
```

Use the authenticated service object to query data from Google Search Console. Define the site URL, date range, and dimensions you wish to retrieve. Here's an example of how to query the API:

```python
site_url = 'https://www.example.com' # Replace with your site URL
request = {
'startDate': '2023-01-01',
'endDate': '2023-01-31',
'dimensions': ['query']
}

response = service.searchanalytics().query(siteUrl=site_url, body=request).execute()
data = response.get('rows', [])
```

Once you have the data, transform it into a format suitable for MongoDB. Typically, this involves converting the response data into a list of dictionaries, where each dictionary represents a document to be inserted into MongoDB:

```python
documents = []
for row in data:
document = {
'query': row['keys'][0],
'clicks': row['clicks'],
'impressions': row['impressions'],
'ctr': row['ctr'],
'position': row['position']
}
documents.append(document)
```

Establish a connection to your MongoDB instance. You can use the `pymongo` library to connect to a local or remote MongoDB database. Here’s an example of how to connect and select a database and collection:

```python
from pymongo import MongoClient

client = MongoClient('mongodb://localhost:27017/')
db = client['your_database_name']
collection = db['your_collection_name']
```

Finally, insert the transformed data into MongoDB. Use the `insert_many()` method to add the list of documents to your collection:

```python
if documents:
collection.insert_many(documents)
print(f"Inserted {len(documents)} documents into MongoDB.")
else:
print("No data to insert.")
```

---

By following these steps, you can programmatically retrieve data from Google Search Console and store it in a MongoDB database without relying on third-party connectors. Adjust the scripts as necessary to fit your specific use case and data requirements.