Freshcaller to Mode Integration
AI-first ETL from Freshcaller into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshcaller into Mode
Datrise syncs Freshcaller's calls, agents, queues, phone numbers, call metrics, and recordings 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
Freshcaller: Cloud call center for support and sales.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Freshcaller entities map to Mode
| Freshcaller entity | Mode object | Notes |
|---|---|---|
| calls | freshcaller_calls | id PK · custom fields → flattened columns for SQL and notebooks |
| agents | freshcaller_agents | id PK · linked to freshcaller_calls |
| queues | freshcaller_queues | id PK · linked to freshcaller_calls |
| phone numbers | freshcaller_phone_numbers | id PK · linked to freshcaller_calls |
FAQ
How does Datrise handle Freshcaller'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 Freshcaller to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Freshcaller
Early access
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