DatriseAI-first ETL

Desk.com to Looker Integration

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

How Datrise loads Desk.com into Looker

Datrise syncs Desk.com's cases, customers, interactions, agents, labels, and replies 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

Desk.com: Customer support ticketing and case history.

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

How Desk.com entities map to Looker

Desk.com entityLooker objectNotes
casesdesk_com_casesid PK · custom fields → flattened columns (nested fields expanded for modeling)
customersdesk_com_customersid PK · linked to desk_com_cases
interactionsdesk_com_interactionsid PK · linked to desk_com_cases
agentsdesk_com_agentsid PK · linked to desk_com_cases

FAQ

How does Datrise handle Desk.com'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 Desk.com to Looker sync stay up to date?

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

Related pipelines

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