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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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Google Search Console is a Google service that helps site owners get the most out of their website. It offers ways for site owners to monitor, troubleshoot, and improve a site’s position on Google Search. It also provides reports and tools for measuring a site’s Search performance and traffic; learning what search queries lead to a site; optimizing website content; monitoring, testing, and tracking AMP pages; and much more, including the ability to test a site’s mobile usability.
Google Search Console's API provides access to a wide range of data related to a website's performance in Google search results. The following are the categories of data that can be accessed through the API:
1. Search Analytics: This category includes data related to search queries, impressions, clicks, and click-through rates.
2. Sitemaps: This category includes data related to the sitemap of a website, such as the number of URLs submitted, indexed, and any errors encountered.
3. Crawl Errors: This category includes data related to any crawl errors encountered by Google while crawling a website, such as 404 errors, server errors, and soft 404 errors.
4. Security Issues: This category includes data related to any security issues detected by Google, such as malware or phishing.
5. Indexing: This category includes data related to the indexing status of a website, such as the number of pages indexed and any indexing errors encountered.
6. Structured Data: This category includes data related to the structured data markup on a website, such as the number of pages with structured data and any errors encountered.
7. Mobile Usability: This category includes data related to the mobile usability of a website, such as the number of pages with mobile usability issues and any errors encountered.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
