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To begin, log in to your Google Search Console account. Navigate to the specific property from which you want to extract data. Use the built-in tools to review the performance reports and determine which data you need, such as clicks, impressions, CTR, and position.
Google Search Console allows you to export your data in CSV format. Within the performance report section, use the "Export" option to download the data. Select "CSV" as your export format. This will be your raw data file that you will import into your Oracle Database.
Ensure that your Oracle Database is set up and accessible. Create a new table or prepare an existing one to store the Google Search Console data. Define the table schema according to the data structure of the CSV file, including columns for clicks, impressions, CTR, and position.
Oracle SQL*Loader is a utility to load data from external files into an Oracle Database. Configure SQL*Loader by creating a control file (.ctl) that describes how to interpret the CSV data. This file should define the data fields and their corresponding columns in the Oracle table.
Transfer the CSV file to your Oracle Database server. You can use secure file transfer methods such as SCP or SFTP to ensure the file is safely moved to the server environment where SQL*Loader can access it.
Execute the SQL*Loader utility with the control file and CSV file as inputs. This will import the data from the CSV into the specified Oracle table. Ensure the command is correctly pointing to the control and data files, and monitor for any errors during the loading process.
After loading the data, verify that it has been accurately imported into the Oracle Database. Run SQL queries to validate the data against the original CSV file, checking for consistency in key metrics like total clicks and impressions. Make any necessary adjustments to the table or data as needed.
By following these steps, you can manually move data from Google Search Console to an Oracle Database 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:
