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Access Google PageSpeed Insights through its web interface or API. If using the API, send a request to the PageSpeed Insights endpoint with the necessary parameters (like the URL and strategy). Capture the JSON response which contains the performance data for the specified webpage.
Parse the JSON response to extract the data fields you need. This could include metrics such as First Contentful Paint, Speed Index, Largest Contentful Paint, etc. Use a programming language like Python or JavaScript to automate this extraction process.
Once you have the necessary data fields, format this information into a CSV (Comma-Separated Values) file. This can be done using libraries such as Python’s `csv` module. Ensure that each performance metric is a column and each tested URL is a row in your CSV file.
Log into your Starburst Galaxy account and create a new schema or table if necessary. Ensure that the table structure matches the columns of your CSV file, so the data can be ingested smoothly. Define the data types for each column according to the data extracted.
Store the CSV file in a cloud storage service that Starburst Galaxy can access, such as Amazon S3. You’ll need to upload the file to an S3 bucket and ensure the correct permissions are set so that Starburst Galaxy can read the file.
Use SQL commands in Starburst Galaxy to load data from the cloud storage into your table. For example, execute a `CREATE TABLE AS SELECT` statement or use the `COPY` command, specifying the S3 path to the CSV file. Ensure that the necessary access credentials and configurations are provided for the cloud storage.
After loading the data, run a few queries in Starburst Galaxy to verify that the data has been transferred correctly and completely. Check for data integrity by comparing a few sample records with the original data from Google PageSpeed Insights.
By following these steps, you can efficiently move data from Google PageSpeed Insights to Starburst Galaxy 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 PageSpeed Insights is a tool that analyzes the performance of a website on both mobile and desktop devices. It provides a score out of 100 for the website's speed and optimization, as well as suggestions for improving the website's performance. The tool measures various factors such as page load time, time to first byte, and the number of requests made by the website. It also provides recommendations for optimizing images, reducing server response time, and minimizing render-blocking resources. The goal of PageSpeed Insights is to help website owners improve their website's speed and user experience, which can lead to higher search engine rankings and increased user engagement.
Google PageSpeed Insights API provides access to a wide range of data related to website performance. The API offers both mobile and desktop performance metrics, including:
• Page load time
• Time to first byte
• First contentful paint
• Speed index
• Time to interactive
• Total blocking time
• Cumulative layout shift
• Opportunities for improvement
• Diagnostics for common performance issues
• Suggestions for optimizing website performance
The API also provides data on the following categories:
• Resource loading times
• Image optimization
• JavaScript and CSS optimization
• Server response time
• Browser caching
• Compression
• Render-blocking resources
• Minification
Overall, the Google PageSpeed Insights API provides developers with a comprehensive set of data to help them optimize website performance and improve user experience.
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:
