Pendo → Sisense
AI-first ETL from Pendo into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Pendo into Sisense
Datrise syncs Pendo's events, guides, NPS, feature adoption, and account metadata into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
Pendo: Product analytics and in-app guidance for SaaS teams.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Pendo entities map to Sisense
| Pendo entity | Sisense object | Notes |
|---|---|---|
| events | pendo_events | date/time fields events |
| guides | pendo_guides | id PK · linked to pendo_events |
| NPS | pendo_nps | id PK · linked to pendo_events |
| feature adoption | pendo_feature_adoption | id PK · linked to pendo_events |
FAQ
How does Datrise handle Pendo's custom fields in Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the Pendo to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
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