Build Apache Iceberg Tables on AWS Without a Cluster
Hands on: Build and refresh Amazon S3 Tables with dbt and DuckDB, then query them in Athena.


Configure dbt and DuckDB to write Iceberg tables to Amazon S3 Tables through its REST catalog.
Build and refresh managed Iceberg tables with dbt models running on DuckDB.
Verify the tables in Amazon S3 Tables and query them from Athena.
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Teams increasingly use dbt and Iceberg to build modular, portable data assets. But writing dbt models to a managed Iceberg catalog has often required a Spark cluster or cloud data warehouse. For a scheduled refresh, small transformation, or proof of concept, that can mean more infrastructure than the job needs.
This hands-on lab shows what Fivetran + dbt Labs can offer AWS shops, specifically, how DuckDB can serve as a lightweight, single-node write engine for materializing dbt models in Amazon S3 Tables, without provisioning a cluster or warehouse for the job. You’ll configure the Iceberg REST catalog, build two tables, and verify that other engines can read them.
Configure dbt and DuckDB to write Iceberg tables to Amazon S3 Tables through its REST catalog.
Build and refresh managed Iceberg tables with dbt models running on DuckDB.
Verify the tables in Amazon S3 Tables and query them from Athena.


Teams increasingly use dbt and Iceberg to build modular, portable data assets. But writing dbt models to a managed Iceberg catalog has often required a Spark cluster or cloud data warehouse. For a scheduled refresh, small transformation, or proof of concept, that can mean more infrastructure than the job needs.
This hands-on lab shows what Fivetran + dbt Labs can offer AWS shops, specifically, how DuckDB can serve as a lightweight, single-node write engine for materializing dbt models in Amazon S3 Tables, without provisioning a cluster or warehouse for the job. You’ll configure the Iceberg REST catalog, build two tables, and verify that other engines can read them.

