Snowplow to ThoughtSpot Integration
AI-first ETL from Snowplow into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into ThoughtSpot
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.
Ideal for natural-language search analytics over a warehouse.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Snowplow entities map to ThoughtSpot
| Snowplow entity | ThoughtSpot object | Notes |
|---|---|---|
| events | snowplow_events | date/time columns events |
| users | snowplow_users | id PK · linked to snowplow_events |
| sessions | snowplow_sessions | id PK · linked to snowplow_events |
| contexts | snowplow_contexts | id PK · linked to snowplow_events |
FAQ
How does Datrise handle Snowplow's custom fields in ThoughtSpot?
Flexible values are stored as flattened columns for searchable fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ThoughtSpot types.
How does the Snowplow to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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