DatriseAI-first ETL

Zendesk Talk to Chartio Integration

AI-first ETL from Zendesk Talk into Chartio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Zendesk Talk into Chartio

Datrise syncs Zendesk Talk's calls, agents, queues, call legs, recordings, and call metrics 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

Zendesk Talk: Voice support calls and queue performance.

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Zendesk Talk entities map to Chartio

Zendesk Talk entityChartio objectNotes
callszendesk_talk_callsid PK · custom fields → flattened columns for visual SQL
agentszendesk_talk_agentsid PK · linked to zendesk_talk_calls
queueszendesk_talk_queuesid PK · linked to zendesk_talk_calls
call legszendesk_talk_call_legsid PK · linked to zendesk_talk_calls

FAQ

How does Datrise handle Zendesk Talk'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 Zendesk Talk to Chartio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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