Segment → DuckDB
AI-first ETL from Segment into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Segment into DuckDB
Datrise syncs Segment's sources, destinations, track events, identify calls, and schema catalog 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
Segment: Customer data platform routing events to warehouses.
DuckDB: In-process analytics database for fast local OLAP.
How Segment entities map to DuckDB
| Segment entity | DuckDB object | Notes |
|---|---|---|
| sources | segment_sources | id PK · custom fields → JSON or STRUCT columns |
| destinations | segment_destinations | id PK · linked to segment_sources |
| track events | segment_track_events | TIMESTAMP WITH TIME ZONE events |
| identify calls | segment_identify_calls | id PK · linked to segment_sources |
FAQ
How does Datrise handle Segment'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 Segment 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 Segment
Early access
Connect Segment to DuckDB the easy way
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