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Log in to your Google Search Console account using your Google credentials. Ensure you have the necessary permissions to access the property from which you want to export data. Navigate to the desired property and select the "Performance" report from the left-hand menu.
Customize the performance report by selecting the desired date range, search type (e.g., Web, Image, or Video), and any other filters or dimensions (such as queries, pages, countries, devices, etc.) that are relevant to the data you wish to export.
Once you have customized your data view, click on the "EXPORT" button located in the top-right corner of the performance report. Choose the "Google Sheets" option from the dropdown menu. This action will automatically create a new Google Sheet with your selected data.
After the export is complete, a new Google Sheet will open automatically in a new tab. The exported data will be organized into columns according to the dimensions and metrics you selected in the Search Console performance report.
Review the data in the Google Sheet to ensure it is complete and formatted to your preference. You may want to adjust column widths, apply filters, or use conditional formatting to make the data easier to analyze and interpret.
Use Google Sheets' built-in functions and tools to analyze the data. You can create pivot tables, charts, and graphs to visualize trends, calculate metrics like averages or growth rates, and derive insights from the data.
To streamline future data updates, you can create a script using Google Apps Script to automate the export and refresh process. This requires some knowledge of scripting and the Google Sheets API. Write a script that periodically clears the existing data and imports updated data from Google Search Console into your Google Sheet.
By following these steps, you can effectively move data from Google Search Console to Google Sheets manually, allowing for further analysis and reporting without relying on third-party tools.
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: