How to load data from Google Search Console to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Google Search Console data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Google Search Console connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Google Search Console data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Google Search Console to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Set Up Google Search Console API Access

To begin, you'll need to enable the Google Search Console API. Go to the Google Cloud Console, create a new project, and enable the Google Search Console API for this project. After enabling the API, create credentials (OAuth 2.0 Client ID) that will be used to authenticate and authorize access to the Search Console data.

Use the OAuth 2.0 Client ID to authenticate and obtain an access token. This can be done using a Python script or any other language you are comfortable with that supports HTTP requests. The access token allows you to make authorized requests to the Google Search Console API to fetch data.

With the access token, construct HTTP requests to the Google Search Console API to fetch the desired data. You can specify the site URL, date range, dimensions, and metrics you want to retrieve. Use the `searchanalytics.query` method to gather the data.

Once the data is retrieved, save it locally in a CSV file. This can be done using a simple script that writes the data to a file. Make sure to format the CSV correctly with headers for the columns (dimensions and metrics) you have selected.

Access your Databricks workspace and create a new cluster if you don't have one already. Ensure that the cluster is running and you have the necessary permissions to upload data and run notebooks.

Use the Databricks web interface or the Databricks CLI to upload the CSV file from your local machine to the Databricks File System (DBFS). The file can be stored in a specific directory within DBFS for easy access.

Create a new Databricks notebook and write a script to load the CSV data into a Delta Lake table. Use PySpark or Scala to read the CSV file from DBFS, process the data if necessary, and write it to a Delta Lake table for further analysis and querying within the Databricks Lakehouse environment.

By following these steps, you can successfully move data from Google Search Console to Databricks Lakehouse without relying on third-party connectors or integrations.