Apollo → Birst
AI-first ETL from Apollo into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Birst
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Apollo entities map to Birst
| Apollo entity | Birst object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → flattened columns |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | date/time dimensions events |
FAQ
How does Datrise handle Apollo's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Apollo to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
More destinations for Apollo
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