Google PageSpeed Insights to Sisense Integration

How Datrise loads Google PageSpeed Insights into Sisense

Datrise syncs Google PageSpeed Insights's audits, lab metrics, field metrics, opportunities, page URLs, and performance scores into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

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

Sisense: Analytics platform with elastic data models and embedded analytics.

How Google PageSpeed Insights entities map to Sisense

Google PageSpeed Insights entity	Sisense object	Notes
audits	`google_pagespeed_insights_audits`	id PK · custom fields → flattened columns for the cube
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 Sisense?

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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