DatriseAI-first ETL

Customer.io Mode

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

How Datrise loads Customer.io into Mode

Datrise syncs Customer.io's profiles, segments, campaigns, deliveries, and conversion events into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

Customer.io: Messaging automation based on product and behavioral data.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Customer.io entities map to Mode

Customer.io entityMode objectNotes
profilescustomer_io_profilesid PK · custom fields → flattened columns for SQL and notebooks
segmentscustomer_io_segmentsid PK · linked to customer_io_profiles
campaignscustomer_io_campaignsid PK · linked to customer_io_profiles
deliveriescustomer_io_deliveriesid PK · linked to customer_io_profiles

FAQ

How does Datrise handle Customer.io's custom fields in Mode?

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

How does the Customer.io to Mode sync stay up to date?

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

Related pipelines

Early access

Connect Customer.io to Mode the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.