How to load data from Google PageSpeed Insights to Weaviate

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

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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 Weaviate 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 Weaviate 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: Access Google PageSpeed Insights API

Begin by obtaining access to the Google PageSpeed Insights API. You'll need to generate an API key by creating a project in the Google Cloud Console, enabling the PageSpeed Insights API, and configuring the necessary credentials. Once you have the API key, you can use it to make requests to the API for website performance data.

Use the API key to make HTTP GET requests to the PageSpeed Insights API. You can use tools like `curl` or libraries like `requests` in Python to fetch the data. Ensure you specify the URL of the website you want to analyze, the strategy (desktop or mobile), and include your API key in the request. Capture the JSON response, which contains performance metrics and insights.

Once you have the JSON response from PageSpeed Insights, parse the JSON to extract relevant data points. This could include metrics like First Contentful Paint, Speed Index, Time to Interactive, and others. Use a programming language like Python to handle JSON parsing efficiently, extracting the specific keys and values you need for analysis.

Prepare the extracted data to match a schema compatible with Weaviate. Weaviate is a vector search engine that organizes data objects and their semantic relationships. Decide on the object classes and properties you'll need in Weaviate to store the PageSpeed Insights data. This might involve converting numerical values into vectors if required by your use case.

Install and configure an instance of Weaviate. You can do this locally or by setting up a cloud-hosted instance. Follow the Weaviate documentation to ensure your instance is correctly configured, paying attention to schema definitions that align with your data transformation plan. Ensure you have the necessary access credentials to interact with the Weaviate API.

Use Weaviate's RESTful API to input the transformed data. This involves making HTTP POST requests to create objects in Weaviate. Use the `/v1/objects` endpoint to upload each data object, ensuring it adheres to the classes and properties defined in your Weaviate schema. Handle any errors or conflicts as per Weaviate's API guidelines.

After uploading the data, verify that it has been correctly stored in Weaviate. Use the Weaviate API to query the data and ensure it matches your expectations. Test different queries to confirm that the data relationships and properties are correctly set up. This step ensures the integrity and usability of your data within the Weaviate environment.

By following these steps, you will manually transfer data from Google PageSpeed Insights to Weaviate without relying on third-party connectors or integrations.