Apollo → Chartio
AI-first ETL from Apollo into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Chartio
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Apollo entities map to Chartio
| Apollo entity | Chartio object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → flattened columns for visual SQL |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | temporal columns events |
FAQ
How does Datrise handle Apollo's custom fields in Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Apollo to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Apollo
Early access
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