DatriseAI-first ETL

Youtube Analytics Neon

AI-first ETL from Youtube Analytics into Neon. Governed entities, incremental sync, typed landing tables.

How Datrise loads Youtube Analytics into Neon

Datrise syncs Youtube Analytics'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

Youtube Analytics: SaaS or API data source for analytics and warehouse sync.

Neon: Serverless Postgres destination with branching and autoscaling.

How Youtube Analytics entities map to Neon

Youtube Analytics entityNeon objectNotes
recordsyoutube_analytics_recordsid PK · custom fields → jsonb columns
eventsyoutube_analytics_eventstimestamptz events
configuration objectsyoutube_analytics_configuration_objectsid PK · linked to youtube_analytics_records

FAQ

How does Datrise handle Youtube Analytics'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 Youtube Analytics 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

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