DatriseAI-first ETL

Firebase to Databricks SQL Warehouse Integration

AI-first ETL from Firebase into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Firebase into Databricks SQL Warehouse

Datrise syncs Firebase's users, collections, documents, auth events, app events, and security rules into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Firebase: App backend with auth, storage, and analytics.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Firebase entities map to Databricks SQL Warehouse

Firebase entityDatabricks SQL Warehouse objectNotes
usersfirebase_usersid PK · custom fields → VARIANT or STRUCT columns
collectionsfirebase_collectionsid PK · linked to firebase_users
documentsfirebase_documentsid PK · linked to firebase_users
auth eventsfirebase_auth_eventsTIMESTAMP events

FAQ

How does Datrise handle Firebase's custom fields in Databricks SQL Warehouse?

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

How does the Firebase to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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