DatriseAI-first ETL

Fauna to PostgreSQL Integration

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

How Datrise loads Fauna into PostgreSQL

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history into PostgreSQL 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 each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

Fauna: Distributed document-relational database.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How Fauna entities map to PostgreSQL

Fauna entityPostgreSQL 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 PostgreSQL?

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 PostgreSQL types.

How does the Fauna to PostgreSQL sync stay up to date?

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

Related pipelines

Early access

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