Heap → Amazon QuickSight
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 records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.
Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.
How Heap entities map to Amazon QuickSight
| Heap entity | Amazon QuickSight object | Notes |
|---|---|---|
| records | heap_records | id PK · custom fields → flattened columns for analyses |
| events | heap_events | date/time fields events |
| configuration objects | heap_configuration_objects | id PK · linked to heap_records |
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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Early access
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