DatriseAI-first ETL

Zendesk Chat Yellowfin

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

How Datrise loads Zendesk Chat into Yellowfin

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

Zendesk Chat: Live chat conversations and agent performance.

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How Zendesk Chat entities map to Yellowfin

Zendesk Chat entityYellowfin objectNotes
chatszendesk_chat_chatsid PK · custom fields → flattened columns
agentszendesk_chat_agentsid PK · linked to zendesk_chat_chats
visitorszendesk_chat_visitorsid PK · linked to zendesk_chat_chats
departmentszendesk_chat_departmentsid PK · linked to zendesk_chat_chats

FAQ

How does Datrise handle Zendesk Chat's custom fields in Yellowfin?

Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Yellowfin types.

How does the Zendesk Chat to Yellowfin sync stay up to date?

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

Related pipelines

Early access

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