DatriseAI-first ETL

3PL Central to DuckDB Integration

AI-first ETL from 3PL Central into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads 3PL Central into DuckDB

Datrise syncs 3PL Central's orders, receipts, inventory levels, items, customers, and warehouses 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

3PL Central: Third-party logistics warehouse and order operations.

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

How 3PL Central entities map to DuckDB

3PL Central entityDuckDB objectNotes
orderstplcentral_ordersid PK · custom fields → JSON or STRUCT columns
receiptstplcentral_receiptsid PK · linked to tplcentral_orders
inventory levelstplcentral_inventory_levelsid PK · linked to tplcentral_orders
itemstplcentral_itemsid PK · linked to tplcentral_orders

FAQ

How does Datrise handle 3PL Central'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 3PL Central 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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