DatriseAI-first ETL

Day.ai Looker Studio

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

How Datrise loads Day.ai into Looker Studio

Datrise syncs Day.ai's contacts, accounts, deals, activities, and lifecycle events 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

Day.ai: AI-native CRM for relationship data, enrichment, and workflow automation.

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

How Day.ai entities map to Looker Studio

Day.ai entityLooker Studio objectNotes
contactsday_ai_contactsid PK · custom fields → flattened columns for chart fields
accountsday_ai_accountsid PK · linked to day_ai_contacts
dealsday_ai_dealsid PK · linked to day_ai_contacts
activitiesday_ai_activitiesdate dimension columns events

FAQ

How does Datrise handle Day.ai'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 Day.ai to Looker Studio sync stay up to date?

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

Related pipelines

Early access

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