Whisky Hunter → Sisense
AI-first ETL from Whisky Hunter into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Whisky Hunter into Sisense
Datrise syncs Whisky Hunter's records, events, and configuration objects 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
Whisky Hunter: SaaS or API data source for analytics and warehouse sync.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Whisky Hunter entities map to Sisense
| Whisky Hunter entity | Sisense object | Notes |
|---|---|---|
| records | whisky_hunter_records | id PK · custom fields → flattened columns for the cube |
| events | whisky_hunter_events | date/time fields events |
| configuration objects | whisky_hunter_configuration_objects | id PK · linked to whisky_hunter_records |
FAQ
How does Datrise handle Whisky Hunter'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 Whisky Hunter to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
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