How to load data from Google PageSpeed Insights to Databricks Lakehouse

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

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Set up a Google PageSpeed Insights connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Google PageSpeed Insights data

Select Databricks Lakehouse where you want to import data from your Google PageSpeed Insights 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 Databricks Lakehouse 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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TL;DR

set up Google PageSpeed Insights as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Google PageSpeed Insights

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.

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Google PageSpeed Insights with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Google PageSpeed Insights account to transfer your customer data automatically from.
A Databricks Lakehouse account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Google PageSpeed Insights and Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from Google PageSpeed Insights to Databricks Lakehouse, it extracts data from Google PageSpeed Insights using the source connector, converts it into a format Databricks Lakehouse can ingest using the provided schema, and then loads it into Databricks Lakehouse via the destination connector. This allows businesses to leverage their Google PageSpeed Insights data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Google PageSpeed Insights as a source connector

1. Go to the Airbyte website and sign up for an account.
2. Once you have signed up, log in to your account and click on the ""Sources"" tab.
3. Scroll down until you find the ""Google PageSpeed Insights"" source connector and click on it.
4. Click on the ""Create Connection"" button to start setting up your connection.
5. Enter your Google PageSpeed Insights API key in the appropriate field.
6. Choose the website you want to connect to from the dropdown menu.
7. Click on the ""Test"" button to make sure your connection is working properly.
8. If the test is successful, click on the ""Save & Sync"" button to save your connection and start syncing your data.
9. You can now view your Google PageSpeed Insights data in Airbyte and use it to analyze your website's performance.

Step 2: Set up Databricks Lakehouse as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

Step 3: Set up a connection to sync your Google PageSpeed Insights data to Databricks Lakehouse

Once you've successfully connected Google PageSpeed Insights as a data source and Databricks Lakehouse as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Google PageSpeed Insights from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Google PageSpeed Insights objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Google PageSpeed Insights to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your Google PageSpeed Insights data.

Use Cases to transfer your Google PageSpeed Insights data to Databricks Lakehouse

Integrating data from Google PageSpeed Insights to Databricks Lakehouse provides several benefits. Here are a few use cases:

Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Google PageSpeed Insights data, extracting insights that wouldn't be possible within Google PageSpeed Insights alone.
Data Consolidation: If you're using multiple other sources along with Google PageSpeed Insights, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Google PageSpeed Insights has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Google PageSpeed Insights data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Google PageSpeed Insights data.
Data Science and Machine Learning: By having Google PageSpeed Insights data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Google PageSpeed Insights provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a Google PageSpeed Insights table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Google PageSpeed Insights account as an Airbyte data source connector.
Configure Databricks Lakehouse as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Google PageSpeed Insights to Databricks Lakehouse after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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Sync with Airbyte

Step 1: Set up Google PageSpeed Insights as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

Step 3: Set up a connection to sync your Google PageSpeed Insights data to Databricks Lakehouse

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Google PageSpeed Insights from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Google PageSpeed Insights objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Google PageSpeed Insights to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your Google PageSpeed Insights data.

How to Sync Google PageSpeed Insights to Databricks Lakehouse Manually

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Step 7:

Step 8:

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.

What is Google PageSpeed Insights?

What data can you extract from Google PageSpeed Insights?

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.

How do I transfer data from Google PageSpeed Insights?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Google PageSpeed Insights to Databricks Lakehouse as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Google PageSpeed Insights to Databricks Lakehouse and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

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Warehouses and Lakes

Marketing Analytics

How to load data from Google PageSpeed Insights to Databricks Lakehouse

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Google PageSpeed Insights

What is Databricks Lakehouse

Prerequisites

Step 1: Setup Google PageSpeed Insights

Step 2: Setup Databricks Lakehouse

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Google PageSpeed Insights and Databricks Lakehouse

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Google PageSpeed Insights as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Google PageSpeed Insights

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Google PageSpeed Insights with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Google PageSpeed Insights account to transfer your customer data automatically from.
A Databricks Lakehouse account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up Google PageSpeed Insights as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

Step 3: Set up a connection to sync your Google PageSpeed Insights data to Databricks Lakehouse

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Google PageSpeed Insights from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Google PageSpeed Insights objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Google PageSpeed Insights to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your Google PageSpeed Insights data.

Use Cases to transfer your Google PageSpeed Insights data to Databricks Lakehouse

Integrating data from Google PageSpeed Insights to Databricks Lakehouse provides several benefits. Here are a few use cases:

Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Google PageSpeed Insights data, extracting insights that wouldn't be possible within Google PageSpeed Insights alone.
Data Consolidation: If you're using multiple other sources along with Google PageSpeed Insights, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Google PageSpeed Insights has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Google PageSpeed Insights data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Google PageSpeed Insights data.
Data Science and Machine Learning: By having Google PageSpeed Insights data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Google PageSpeed Insights provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a Google PageSpeed Insights table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.