DatriseAI-first ETL

Firebase Realtime Database Neon

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

How Datrise loads Firebase Realtime Database into Neon

Datrise syncs Firebase Realtime Database's records, events, and configuration objects 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

Firebase Realtime Database: SaaS or API data source for analytics and warehouse sync.

Neon: Serverless Postgres destination with branching and autoscaling.

How Firebase Realtime Database entities map to Neon

Firebase Realtime Database entityNeon objectNotes
recordsfirebase_realtime_database_recordsid PK · custom fields → jsonb columns
eventsfirebase_realtime_database_eventstimestamptz events
configuration objectsfirebase_realtime_database_configuration_objectsid PK · linked to firebase_realtime_database_records

FAQ

How does Datrise handle Firebase Realtime Database'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 Firebase Realtime Database 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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