PostHog to Spotfire Integration
AI-first ETL from PostHog into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads PostHog into Spotfire
Datrise syncs PostHog's events, persons, sessions, feature flags, cohorts, and insights 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
PostHog: Product analytics with session replay and flags.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How PostHog entities map to Spotfire
| PostHog entity | Spotfire object | Notes |
|---|---|---|
| events | posthog_events | date/time columns events |
| persons | posthog_persons | id PK · linked to posthog_events |
| sessions | posthog_sessions | id PK · linked to posthog_events |
| feature flags | posthog_feature_flags | id PK · linked to posthog_events |
FAQ
How does Datrise handle PostHog'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 PostHog 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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