DatriseAI-first ETL

Zendesk Looker

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

How Datrise loads Zendesk into Looker

Datrise syncs Zendesk's tickets, users, organizations, macros, and satisfaction ratings into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Zendesk: Customer support suite with tickets and knowledge base.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Zendesk entities map to Looker

Zendesk entityLooker objectNotes
ticketszendesk_ticketsid PK · custom fields → flattened columns (nested fields expanded for modeling)
userszendesk_usersid PK · linked to zendesk_tickets
organizationszendesk_organizationsid PK · linked to zendesk_tickets
macroszendesk_macrosid PK · linked to zendesk_tickets

FAQ

How does Datrise handle Zendesk's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Zendesk to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

Early access

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