Segment → Mode
AI-first ETL from Segment into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Segment into Mode
Datrise syncs Segment's sources, destinations, track events, identify calls, and schema catalog 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
Segment: Customer data platform routing events to warehouses.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Segment entities map to Mode
| Segment entity | Mode object | Notes |
|---|---|---|
| sources | segment_sources | id PK · custom fields → flattened columns for SQL and notebooks |
| destinations | segment_destinations | id PK · linked to segment_sources |
| track events | segment_track_events | temporal columns events |
| identify calls | segment_identify_calls | id PK · linked to segment_sources |
FAQ
How does Datrise handle Segment'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 Segment to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Segment
Early access
Connect Segment to Mode the easy way
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