DatriseAI-first ETL

Firebase Realtime Database Mode

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

How Datrise loads Firebase Realtime Database into Mode

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

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

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

How Firebase Realtime Database entities map to Mode

Firebase Realtime Database entityMode objectNotes
recordsfirebase_realtime_database_recordsid PK · custom fields → flattened columns for SQL and notebooks
eventsfirebase_realtime_database_eventstemporal columns 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 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 Firebase Realtime Database to Mode sync stay up to date?

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

Related pipelines

Early access

Connect Firebase Realtime Database to Mode the easy way

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