DatriseAI-first ETL

EngageBay to Looker Integration

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

How Datrise loads EngageBay into Looker

Datrise syncs EngageBay's contacts, deals, tickets, sequences, forms, and landing pages 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

EngageBay: All-in-one CRM with marketing, sales, and service bays.

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

How EngageBay entities map to Looker

EngageBay entityLooker objectNotes
contactsengagebay_contactsid PK · custom fields → flattened columns (nested fields expanded for modeling)
dealsengagebay_dealsid PK · linked to engagebay_contacts
ticketsengagebay_ticketsid PK · linked to engagebay_contacts
sequencesengagebay_sequencesid PK · linked to engagebay_contacts

FAQ

How does Datrise handle EngageBay'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 EngageBay 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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