Oracle Database to DuckDB Integration

How Datrise loads Oracle Database into DuckDB

Datrise syncs Oracle Database's tables, rows, schemas, indexes, sequences, and redo log changes 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

Oracle Database: Oracle Database for enterprise transactional workloads.

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

How Oracle Database entities map to DuckDB

Oracle Database entity	DuckDB object	Notes
tables	`oracle_db_tables`	id PK · custom fields → JSON or STRUCT columns
rows	`oracle_db_rows`	id PK · linked to oracle_db_tables
schemas	`oracle_db_schemas`	id PK · linked to oracle_db_tables
indexes	`oracle_db_indexes`	id PK · linked to oracle_db_tables

FAQ

How does Datrise handle Oracle Database'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 Oracle Database 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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