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Log into your Google Search Console account. Navigate to the property from which you want to extract data. Ensure you have the necessary permissions to access and download data from the console.
Within your chosen property in Google Search Console, go to the "Performance" report. Here, you can select the specific data you want to export, such as queries, pages, countries, devices, etc. Use the "Export" button to download the data, choosing either CSV or Google Sheets format.
Open the exported CSV file or Google Sheet. Review the data to ensure it is complete and organized as needed. Clean the data by removing any unnecessary columns or rows, fixing any formatting issues, and ensuring consistency in the data structure.
Log into your Convex account and create a new project or select an existing one where you want to import the Google Search Console data. Ensure you have the necessary permissions to modify and upload data to the project.
Convex requires data in a specific format, typically JSON. Use a script (in Python, JavaScript, or another language you are comfortable with) to convert the cleaned CSV or Google Sheets data into JSON format. Ensure that the JSON structure aligns with how data is organized within Convex.
Once your data is converted into the appropriate JSON format, use Convex's API or console to upload the data. If using the API, write a script that authenticates with Convex and sends POST requests with your JSON data to the correct endpoint. Follow Convex's documentation for any specific requirements or headers needed for the upload process.
After uploading, verify the integrity of the data in Convex. Check random entries or summaries to ensure that data has been accurately transferred without loss or corruption. If discrepancies are found, revisit your conversion and upload steps to troubleshoot and rectify any issues.
By following these steps, you can manually move data from Google Search Console to Convex 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?
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