Zendesk Chat → Mode
AI-first ETL from Zendesk Chat into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Zendesk Chat into Mode
Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times 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 Chat: Live chat conversations and agent performance.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Zendesk Chat entities map to Mode
| Zendesk Chat entity | Mode object | Notes |
|---|---|---|
| chats | zendesk_chat_chats | id PK · custom fields → flattened columns for SQL and notebooks |
| agents | zendesk_chat_agents | id PK · linked to zendesk_chat_chats |
| visitors | zendesk_chat_visitors | id PK · linked to zendesk_chat_chats |
| departments | zendesk_chat_departments | id PK · linked to zendesk_chat_chats |
FAQ
How does Datrise handle Zendesk Chat'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 Chat to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Zendesk Chat
Early access
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