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Start by manually running a PageSpeed Insights analysis for your desired URLs. Navigate to the PageSpeed Insights website, enter the URL you wish to analyze, and run the test. Once the analysis is complete, download the results in JSON format from the API response or copy them for further processing.
Use a programming language like Python to parse the JSON data obtained from PageSpeed Insights. You can use libraries such as `json` in Python to load and manipulate the data. This step involves extracting the relevant metrics and details you want to import into BigQuery, structuring them into a tabular format.
Convert the parsed JSON data into a CSV format. Create a list of dictionaries or a pandas DataFrame (using Python) where each dictionary or row corresponds to a set of metrics for a URL. Ensure that each metric you wish to import into BigQuery is represented as a column.
Use Python to export the structured data into a CSV file. Utilize the `pandas` library to write the DataFrame to a CSV file with a command like `dataframe.to_csv('output.csv', index=False)`. This CSV will serve as the source file for importing data into BigQuery.
Ensure you have a Google Cloud Platform (GCP) project set up with billing enabled. If not, create a new project and enable billing. Then, enable the BigQuery API for your project from the Google Cloud Console.
Use the Google Cloud Console to upload your CSV file to Google Cloud Storage (GCS). Create a new bucket if necessary, and upload your CSV file to this bucket. Note the bucket name and file path, as you will need these details in the next step.
Navigate to BigQuery in the Google Cloud Console. Create a new dataset if you don't have one already. Then, initiate a new table creation process and choose "Create table from Google Cloud Storage." Provide the GCS file path, configure the schema based on your CSV columns, and complete the process to load the data into BigQuery. Now, your PageSpeed Insights data is available in BigQuery for analysis and reporting.
By following these steps, you can manually move data from Google PageSpeed Insights to BigQuery 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:
