Zendesk Talk to Mode Integration
AI-first ETL from Zendesk Talk into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Zendesk Talk into Mode
Datrise syncs Zendesk Talk's calls, agents, queues, call legs, recordings, and call metrics 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
Zendesk Talk: Voice support calls and queue performance.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Zendesk Talk entities map to Mode
| Zendesk Talk entity | Mode object | Notes |
|---|---|---|
| calls | zendesk_talk_calls | id PK · custom fields → flattened columns for SQL and notebooks |
| agents | zendesk_talk_agents | id PK · linked to zendesk_talk_calls |
| queues | zendesk_talk_queues | id PK · linked to zendesk_talk_calls |
| call legs | zendesk_talk_call_legs | id PK · linked to zendesk_talk_calls |
FAQ
How does Datrise handle Zendesk Talk'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 Zendesk Talk to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Zendesk Talk
Early access
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