Amazon S3 to DuckDB Integration

How Datrise loads Amazon S3 into DuckDB

Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes 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 S3: Object storage for files, exports, and data lakes.

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

How Amazon S3 entities map to DuckDB

Amazon S3 entity	DuckDB object	Notes
buckets	`s3_buckets`	id PK · custom fields → JSON or STRUCT columns
objects	`s3_objects`	id PK · linked to s3_buckets
prefixes	`s3_prefixes`	id PK · linked to s3_buckets
object metadata	`s3_object_metadata`	id PK · linked to s3_buckets

FAQ

How does Datrise handle Amazon S3'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 S3 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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