Google PageSpeed Insights to Snowflake Integration
AI-first ETL from Google PageSpeed Insights into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google PageSpeed Insights into Snowflake
Datrise syncs Google PageSpeed Insights's audits, lab metrics, field metrics, opportunities, page URLs, and performance scores into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
Google PageSpeed Insights: Page performance and Core Web Vitals audits.
Snowflake: Cloud data warehouse with separated compute and storage.
How Google PageSpeed Insights entities map to Snowflake
| Google PageSpeed Insights entity | Snowflake object | Notes |
|---|---|---|
| audits | google_pagespeed_insights_audits | id PK · custom fields → VARIANT columns |
| lab metrics | google_pagespeed_insights_lab_metrics | id PK · linked to google_pagespeed_insights_audits |
| field metrics | google_pagespeed_insights_field_metrics | id PK · linked to google_pagespeed_insights_audits |
| opportunities | google_pagespeed_insights_opportunities | id PK · linked to google_pagespeed_insights_audits |
FAQ
How does Datrise handle Google PageSpeed Insights's custom fields in Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the Google PageSpeed Insights to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
Related pipelines
More destinations for Google PageSpeed Insights
- Google PageSpeed Insights → Google BigQuery
- Google PageSpeed Insights → Amazon Redshift
- Google PageSpeed Insights → Databricks SQL Warehouse
- Google PageSpeed Insights → ClickHouse
- Google PageSpeed Insights → DuckDB
- Google PageSpeed Insights → Amazon Athena
- Google PageSpeed Insights → Amazon S3 Data Lake
- Google PageSpeed Insights → Azure Data Lake Storage
- Google PageSpeed Insights → Azure Synapse
- Google PageSpeed Insights → Spreadsheets
- Google PageSpeed Insights → Airtable
- Google PageSpeed Insights → CSV Files
Early access
Connect Google PageSpeed Insights to Snowflake the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.