Reply.io to DuckDB Integration

How Datrise loads Reply.io into DuckDB

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

Reply.io: Multichannel sales engagement sequences.

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

How Reply.io entities map to DuckDB

Reply.io entity	DuckDB object	Notes
campaigns	`reply_io_campaigns`	id PK · custom fields → JSON or STRUCT columns
prospects	`reply_io_prospects`	id PK · linked to reply_io_campaigns
sequences	`reply_io_sequences`	id PK · linked to reply_io_campaigns
email events	`reply_io_email_events`	TIMESTAMP WITH TIME ZONE events

FAQ

How does Datrise handle Reply.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 Reply.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

Connect Reply.io to DuckDB 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.