Braze to DuckDB Integration
AI-first ETL from Braze into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Braze into DuckDB
Datrise syncs Braze's users, campaigns, canvases, message events, segments, and custom attributes 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
Braze: Cross-channel customer engagement platform.
DuckDB: In-process analytics database for fast local OLAP.
How Braze entities map to DuckDB
| Braze entity | DuckDB object | Notes |
|---|---|---|
| users | braze_users | id PK · custom fields → JSON or STRUCT columns |
| campaigns | braze_campaigns | id PK · linked to braze_users |
| canvases | braze_canvases | id PK · linked to braze_users |
| message events | braze_message_events | TIMESTAMP WITH TIME ZONE events |
FAQ
How does Datrise handle Braze'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 Braze 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
More destinations for Braze
Early access
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