Snowplow to Spotfire Integration
AI-first ETL from Snowplow into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Spotfire
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Snowplow entities map to Spotfire
| Snowplow entity | Spotfire 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 Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Snowplow to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
Related pipelines
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