DatriseAI-first ETL

Google Pagespeed Insights Neon

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

How Datrise loads Google Pagespeed Insights into Neon

Datrise syncs Google Pagespeed Insights's records, events, and configuration objects into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.

Ideal for serverless Postgres workloads that scale to zero between syncs.

Endpoints

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

Neon: Serverless Postgres destination with branching and autoscaling.

How Google Pagespeed Insights entities map to Neon

Google Pagespeed Insights entityNeon objectNotes
recordsgoogle_pagespeed_insights_recordsid PK · custom fields → jsonb columns
eventsgoogle_pagespeed_insights_eventstimestamptz 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 Neon?

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

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

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

Early access

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