Apollo → Mode
AI-first ETL from Apollo into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Mode
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Apollo entities map to Mode
| Apollo entity | Mode object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Apollo to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Apollo
Early access
Connect Apollo to Mode the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.