Apollo → Sisense
AI-first ETL from Apollo into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Sisense
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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
Apollo: Sales intelligence and engagement platform with account-level activity.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Apollo entities map to Sisense
| Apollo entity | Sisense object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → flattened columns for the cube |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | date/time fields events |
FAQ
How does Datrise handle Apollo'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 Apollo to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Apollo
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