Smaily to DuckDB Integration

How Datrise loads Smaily into DuckDB

Datrise syncs Smaily's subscribers, campaigns, lists, autoresponders, email events, and segments 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

Smaily: Email and SMS campaigns with subscriber segments.

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

How Smaily entities map to DuckDB

Smaily entity	DuckDB object	Notes
subscribers	`smaily_subscribers`	id PK · custom fields → JSON or STRUCT columns
campaigns	`smaily_campaigns`	id PK · linked to smaily_subscribers
lists	`smaily_lists`	id PK · linked to smaily_subscribers
autoresponders	`smaily_autoresponders`	id PK · linked to smaily_subscribers

FAQ

How does Datrise handle Smaily'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 Smaily 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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