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Connect Google Page Speed to Snowflake

Connect Google Page Speed to Snowflake and export selected accounts, metrics, and dimensions to your Google BigQuery project.

Configure the historical window, destination table, and recurring schedule without maintaining a Google Page Speed API pipeline.

Used by the world's leading companies

Export Google Page Speed data to Snowflake

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Google Page Speed data in PostgreSQL

Bring all your Google Page Speed data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data freshness

Keep your Snowflake tables up to date

Automatically sync new data from Google Page Speed so every dashboard, model, and report works with fresh information.

Analysis

Unify Google Page Speed with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

Google Page Speed metrics and dimensions available in Snowflake

Access 19 metrics and 16 dimensions from Google Page Speed connectors including Performance score, SEO score, First Contentful Paint, Largest Contentful Paint, Cumulative Layout Shift, Total Blocking Time, Speed Index, Page ID, Device, Date, Year month, Week of the year and many more...

19

Metrics availables

16

Dimensions availables

Connect Google Page Speed to Snowflake in minutes

Move your data from Google Page Speed to Snowflake with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your Google Page Speed account

Authenticate your Google Page Speed account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Client A · Connected sources
5 sources ready
Google Page Speed Google Page Speed 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step two

Choose the Snowflake destination

Add the account host, user, and database schema. Use a password for compatible users or a PEM private key for service accounts and stricter policies.

Destination / Snowflake Required fields
Authentication Choose one
Password
Key pair
Host Required
xy12345.eu-west-1
Username Required
catchr_service
Database Required
MARKETING.RAW
Private key passphrase Optional
••••••••••••
Private key Required for key pair
-----BEGIN ENCRYPTED PRIVATE KEY----- MIIE6TAbBgkqhkiG9w0BBQMw... -----END ENCRYPTED PRIVATE KEY-----
Step three

Keep your Snowflake tables updated

Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.

Export job / PostgreSQL Example
Schema Destination
marketing_raw
Table Per job
linkedin_ads_daily
Recurring schedule Choose a cadence
Every 6 hours
Daily · 07:00
Weekly · Mon
First sync scheduledSelected fields · configured history
07:00 UTC

Review 30-day Google PageSpeed website performance in SQL.

This example summarizes performance, accessibility, and Core Web Vitals by page.

Google Page Speed.sql
BigQuery SQL
30-day window
SELECT
  url,
  AVG(performance_score) AS performance_score,
  AVG(accessibility_score) AS accessibility_score,
  AVG(lcp_seconds) AS lcp_seconds
FROM `your_project.marketing_raw.google_pagespeed_pagespeed_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY url
ORDER BY performance_score DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Choose what to load into Snowflake.

Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.

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Before you schedule the job.

Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.

How do I start a scheduled Google Page Speed import into Snowflake?

Start with a connected Google Page Speed source and an existing Snowflake database. Create a datastream between them, then configure the accounts, fields, schedule, and "performance scores" table.

Which Google Page Speed entities can become Snowflake tables?

Select concrete Google Page Speed fields such as Accessibility score, Best Practices score, Bootup Time, and Day of the month. You can rename the chosen columns, preview the result, and write them to Snowflake tables such as sites, pages, and devices.

Does Catchr support an initial fetch for Google Page Speed data in Snowflake?

Set an initial fetch period for Google Page Speed, then choose the frequency and lookback window for the job writing to "performance scores". Catchr rewrites that selected period on each run.

Which partition setting keeps Google Page Speed Snowflake rows updated?

The partition field tells Catchr which Google Page Speed rows in "performance scores" belong to the period being refreshed. If you omit it, every run appends records rather than updating them.

What is a practical SQL use case for Google Page Speed data?

With Google Page Speed data, a Snowflake query can summarize performance, accessibility, and Core Web Vitals by page. A query built around Accessibility score and Best Practices score can support trend reviews, account comparisons, and operational reporting.

Does my Snowflake database need to exist before I connect it?

Yes. Connect an existing Snowflake database first. The Catchr job creates and populates the destination table you configure inside that database.

Can I load several marketing sources into the same Snowflake database?

Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.

Still have a question ? 

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