DatriseAI-first ETL

Google Pagespeed Insights Mode

AI-first ETL from Google Pagespeed Insights into Mode. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Pagespeed Insights into Mode

Datrise syncs Google Pagespeed Insights's records, events, and configuration objects into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

Google Pagespeed Insights: SaaS or API data source for analytics and warehouse sync.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Google Pagespeed Insights entities map to Mode

Google Pagespeed Insights entityMode objectNotes
recordsgoogle_pagespeed_insights_recordsid PK · custom fields → flattened columns for SQL and notebooks
eventsgoogle_pagespeed_insights_eventstemporal columns events
configuration objectsgoogle_pagespeed_insights_configuration_objectsid PK · linked to google_pagespeed_insights_records

FAQ

How does Datrise handle Google Pagespeed Insights's custom fields in Mode?

Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.

How does the Google Pagespeed Insights to Mode sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

Connect Google Pagespeed Insights to Mode the easy way

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