DatriseAI-first ETL

Reply.io to Looker Integration

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

How Datrise loads Reply.io into Looker

Datrise syncs Reply.io's campaigns, prospects, sequences, email events, calls, 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

Reply.io: Multichannel sales engagement sequences.

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

How Reply.io entities map to Looker

Reply.io entityLooker objectNotes
campaignsreply_io_campaignsid PK · custom fields → flattened columns (nested fields expanded for modeling)
prospectsreply_io_prospectsid PK · linked to reply_io_campaigns
sequencesreply_io_sequencesid PK · linked to reply_io_campaigns
email eventsreply_io_email_eventsdate/time dimension columns events

FAQ

How does Datrise handle Reply.io'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 Reply.io 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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