Listrak → DuckDB
AI-first ETL from Listrak into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Listrak into DuckDB
Datrise syncs Listrak's records, events, and configuration objects 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
Listrak: SaaS or API data source for analytics and warehouse sync.
DuckDB: In-process analytics database for fast local OLAP.
How Listrak entities map to DuckDB
| Listrak entity | DuckDB object | Notes |
|---|---|---|
| records | listrak_records | id PK · custom fields → JSON or STRUCT columns |
| events | listrak_events | TIMESTAMP WITH TIME ZONE events |
| configuration objects | listrak_configuration_objects | id PK · linked to listrak_records |
FAQ
How does Datrise handle Listrak'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 Listrak 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
More destinations for Listrak
Early access
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