Amazon RDS to DuckDB Integration

How Datrise loads Amazon RDS into DuckDB

Datrise syncs Amazon RDS's tables, rows, schemas, indexes, primary keys, and change streams 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

Amazon RDS: Managed relational database service on AWS.

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

How Amazon RDS entities map to DuckDB

Amazon RDS entity	DuckDB object	Notes
tables	`amazon_rds_tables`	id PK · custom fields → JSON or STRUCT columns
rows	`amazon_rds_rows`	id PK · linked to amazon_rds_tables
schemas	`amazon_rds_schemas`	id PK · linked to amazon_rds_tables
indexes	`amazon_rds_indexes`	id PK · linked to amazon_rds_tables

FAQ

How does Datrise handle Amazon RDS'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 Amazon RDS 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

Connect Amazon RDS to DuckDB the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.