Google PageSpeed Insights to GoodData Integration

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

How Datrise loads Google PageSpeed Insights into GoodData

Datrise syncs Google PageSpeed Insights's audits, lab metrics, field metrics, opportunities, page URLs, and performance scores into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

Google PageSpeed Insights: Page performance and Core Web Vitals audits.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Google PageSpeed Insights entities map to GoodData

Google PageSpeed Insights entity	GoodData object	Notes
audits	`google_pagespeed_insights_audits`	id PK · custom fields → flattened 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 GoodData?

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

How does the Google PageSpeed Insights to GoodData sync stay up to date?

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

Related pipelines

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Early access

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