Apollo → Looker
AI-first ETL from Apollo into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Looker
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Apollo entities map to Looker
| Apollo entity | Looker object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | date/time dimension columns events |
FAQ
How does Datrise handle Apollo's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Apollo to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Apollo
Early access
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