DatriseAI-first ETL

Zendesk Looker Studio

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

How Datrise loads Zendesk into Looker Studio

Datrise syncs Zendesk's tickets, users, organizations, macros, and satisfaction ratings into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Zendesk: Customer support suite with tickets and knowledge base.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Zendesk entities map to Looker Studio

Zendesk entityLooker Studio objectNotes
ticketszendesk_ticketsid PK · custom fields → flattened columns for chart fields
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 Studio?

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

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

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

Related pipelines

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