DatriseAI-first ETL

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 entitySnowflake objectNotes
auditsgoogle_pagespeed_insights_auditsid PK · custom fields → VARIANT columns
lab metricsgoogle_pagespeed_insights_lab_metricsid PK · linked to google_pagespeed_insights_audits
field metricsgoogle_pagespeed_insights_field_metricsid PK · linked to google_pagespeed_insights_audits
opportunitiesgoogle_pagespeed_insights_opportunitiesid 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

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