How to load data from Google PageSpeed Insights to BigQuery

Learn how to use Airbyte to synchronize your Google PageSpeed Insights data into BigQuery within minutes.

Try Airbyte Cloud Manual method (the hard way)

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Google PageSpeed Insights connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Google PageSpeed Insights data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Google PageSpeed Insights to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync to Manually

Step 1: Extract Data from Google PageSpeed Insights

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.

Step 2: Parse JSON Data

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.

Step 3: Prepare Data for CSV Conversion

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.

Step 4: Export Data to CSV

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.

Step 5: Set Up Google Cloud Project

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.

Step 6: Upload CSV to Google Cloud Storage

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.

Step 7: Load CSV Data into BigQuery

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.