Fauna to Google BigQuery Integration

How Datrise loads Fauna into Google BigQuery

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Fauna: Distributed document-relational database.

Google BigQuery: Serverless analytics warehouse on GCP.

How Fauna entities map to Google BigQuery

Fauna entity	Google BigQuery object	Notes
collections	`fauna_collections`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
documents	`fauna_documents`	id PK · linked to fauna_collections
indexes	`fauna_indexes`	id PK · linked to fauna_collections
functions	`fauna_functions`	id PK · linked to fauna_collections

FAQ

How does Datrise handle Fauna's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Fauna to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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