Google BigQuery to Neon Integration

How Datrise loads Google BigQuery into Neon

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions 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 BigQuery: Serverless analytics warehouse on Google Cloud.

Neon: Serverless Postgres destination with branching and autoscaling.

How Google BigQuery entities map to Neon

Google BigQuery entity	Neon object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → jsonb columns
tables	`bigquery_tables`	id PK · linked to bigquery_datasets
views	`bigquery_views`	id PK · linked to bigquery_datasets
query jobs	`bigquery_query_jobs`	id PK · linked to bigquery_datasets

FAQ

How does Datrise handle Google BigQuery'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 BigQuery 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

Connect Google BigQuery to Neon the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.