Examples

Real dbt projects materialized as Delta tables, plus runnable connection-API showcases — every one rebuilt against live Microsoft Fabric OneLake on each push to main.

The DAGs below, materialized — duckrun writes each dbt model as a Delta table, here in a Microsoft Fabric OneLake lakehouse.

dbt projects

• aemo

  The AEMO dbt project built against live Microsoft Fabric OneLake (abfss://). Full catalog with column metadata over the real Delta tables.

  source: djouallah/dbt_fabric_python_delta

• coffee

  The coffee-shop scenario (coffee_shop): ingest two dimension CSVs over https, dedup the SCD2 product dim, generate an N-row fact partitioned by region, and a revenue mart. Built for real on OneLake, so the catalog carries Delta stats.

  source: JosueBogran/coffeeshopdatageneratorv2

• multi-lakehouse — Bronze/Silver/Gold across three Lakehouses

  One dbt project, a medallion whose layers each materialize into a separate Fabric Lakehouse via the catalogs: profile key + +database: <alias> on a model. Built for real on OneLake; ref() and joins resolve across the Lakehouses.

  source: demo_multicatalog_dbt.py

Pure SQL — the connection API

These are not dbt projects — they're runnable showcases of duckrun.connect(). Click through for every statement and its actual output from a live run.

• taxi — live NYC Yellow-Taxi → Delta

  Reads live NYC TLC Yellow-Taxi data straight off https and lands it into Delta on OneLake using nothing but conn.sql(...) — QUALIFY, PIVOT, ROLLUP, ASOF JOIN, a SQL-only upsert, time travel, and a concurrent-MERGE clash.

  source: demo_taxi.py

• multi-catalog — lakehouse + warehouse + local

  One duckrun.connect() session binding three catalogs: a writable OneLake lakehouse, a read-only Fabric Warehouse (attach(..., read_only=True)), and a local scratch dir. A single conn.sql JOINs across them as catalog.schema.table, the read-only fence refuses a warehouse write, and the mart is written back to the lakehouse.

  source: demo_multicatalog.py