DatriseAI-first ETL

Invoiced to DuckDB Integration

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

How Datrise loads Invoiced into DuckDB

Datrise syncs Invoiced's invoices, customers, payments, subscriptions, credit notes, and line items 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

Invoiced: Accounts receivable automation and billing.

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

How Invoiced entities map to DuckDB

Invoiced entityDuckDB objectNotes
invoicesinvoiced_invoicesid PK · custom fields → JSON or STRUCT columns
customersinvoiced_customersid PK · linked to invoiced_invoices
paymentsinvoiced_paymentsid PK · linked to invoiced_invoices
subscriptionsinvoiced_subscriptionsid PK · linked to invoiced_invoices

FAQ

How does Datrise handle Invoiced'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 Invoiced 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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