Amazon DynamoDB to DuckDB Integration

How Datrise loads Amazon DynamoDB into DuckDB

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records 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 DynamoDB: Serverless key-value and document store on AWS.

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

How Amazon DynamoDB entities map to DuckDB

Amazon DynamoDB entity	DuckDB object	Notes
tables	`source_dynamodb_tables`	id PK · custom fields → JSON or STRUCT columns
items	`source_dynamodb_items`	id PK · linked to source_dynamodb_tables
partition keys	`source_dynamodb_partition_keys`	id PK · linked to source_dynamodb_tables
sort keys	`source_dynamodb_sort_keys`	id PK · linked to source_dynamodb_tables

FAQ

How does Datrise handle Amazon DynamoDB'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 DynamoDB 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

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