DatriseAI-first ETL

Firebase Realtime Database DuckDB

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

How Datrise loads Firebase Realtime Database into DuckDB

Datrise syncs Firebase Realtime Database's records, events, and configuration objects into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

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

DuckDB: In-process analytics database for fast local OLAP.

How Firebase Realtime Database entities map to DuckDB

Firebase Realtime Database entityDuckDB objectNotes
recordsfirebase_realtime_database_recordsid PK · custom fields → JSON or STRUCT columns
eventsfirebase_realtime_database_eventsTIMESTAMP WITH TIME ZONE 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 DuckDB?

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

How does the Firebase Realtime Database to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect Firebase Realtime Database to DuckDB the easy way

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