Heap to Amazon QuickSight Integration
AI-first ETL from Heap into Amazon QuickSight. Governed entities, incremental sync, typed landing tables.
How Datrise loads Heap into Amazon QuickSight
Datrise syncs Heap's events, users, sessions, funnels, segments, and user properties into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.
Ideal for AWS-native dashboards with pay-per-session pricing.
Endpoints
Heap: Autocaptured product analytics and funnels.
Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.
How Heap entities map to Amazon QuickSight
| Heap entity | Amazon QuickSight object | Notes |
|---|---|---|
| events | heap_events | date/time fields events |
| users | heap_users | id PK · linked to heap_events |
| sessions | heap_sessions | id PK · linked to heap_events |
| funnels | heap_funnels | id PK · linked to heap_events |
FAQ
How does Datrise handle Heap's custom fields in Amazon QuickSight?
Flexible values are stored as flattened columns for analyses, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon QuickSight types.
How does the Heap to Amazon QuickSight sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.
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