Customer.io to DuckDB Integration

How Datrise loads Customer.io into DuckDB

Datrise syncs Customer.io's people, segments, campaigns, message events, attributes, and broadcasts into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Customer.io: Behavioral messaging across email, push, and SMS.

DuckDB: In-process analytics database for fast local OLAP.

How Customer.io entities map to DuckDB

Customer.io entity	DuckDB object	Notes
people	`customerio_people`	id PK · custom fields → JSON or STRUCT columns
segments	`customerio_segments`	id PK · linked to customerio_people
campaigns	`customerio_campaigns`	id PK · linked to customerio_people
message events	`customerio_message_events`	TIMESTAMP WITH TIME ZONE events

FAQ

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

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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