DatriseAI-first ETL

Fauna to Neon Integration

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

How Datrise loads Fauna into Neon

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history 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

Fauna: Distributed document-relational database.

Neon: Serverless Postgres destination with branching and autoscaling.

How Fauna entities map to Neon

Fauna entityNeon objectNotes
collectionsfauna_collectionsid PK · custom fields → jsonb columns
documentsfauna_documentsid PK · linked to fauna_collections
indexesfauna_indexesid PK · linked to fauna_collections
functionsfauna_functionsid PK · linked to fauna_collections

FAQ

How does Datrise handle Fauna'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 Fauna 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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