DatriseAI-first ETL

Fauna to Mode Integration

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

How Datrise loads Fauna into Mode

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

Fauna: Distributed document-relational database.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Fauna entities map to Mode

Fauna entityMode objectNotes
collectionsfauna_collectionsid PK · custom fields → flattened columns for SQL and notebooks
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 Mode?

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

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

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

Connect Fauna to Mode 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.