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Begin by logging into your Google Search Console account. Navigate to the performance report section from which you want to extract data. Use the “Export” functionality available in Google Search Console to download the data as CSV or Excel files. This will provide you with a local copy of the data.
Access your AWS Management Console and navigate to the S3 service. Create a new bucket or choose an existing one to store your data. Ensure that the bucket has the appropriate permissions for data upload, such as granting write access to your user or specific IAM roles.
Install the AWS Command Line Interface (CLI) on your local machine if not already installed. Configure it by running `aws configure` in your terminal. Enter your AWS Access Key ID, Secret Access Key, default region, and output format when prompted. This step sets up your local environment to interact with AWS services.
Use the AWS CLI to upload the exported Google Search Console data to your S3 bucket. Execute a command such as `aws s3 cp /path/to/your/local/file.csv s3://your-bucket-name/` to transfer the data file. Confirm the upload by checking your S3 bucket through the AWS Management Console.
Go to the AWS Glue service in your AWS Management Console. Create a new database in AWS Glue to act as a catalog for your data. Then, define a new table within this database that matches the schema of your uploaded data. This step will allow you to organize and prepare your data for querying in AWS services like Athena.
Create a new AWS Glue Crawler that will scan the S3 bucket where you uploaded your data. Configure the crawler to update the table in your Glue database with the schema of your data file. Once set up, run the crawler to catalog the data, making it ready for analysis.
Use AWS Athena to query the data you’ve cataloged in AWS Glue. Navigate to Athena in the AWS Management Console, select the database and table where your data resides, and execute SQL queries to analyze the data. Athena allows you to perform complex queries without needing to set up any additional infrastructure.
By following these steps, you can efficiently move data from Google Search Console to an AWS Data Lake using AWS-native tools and services, avoiding 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:
