Add duckrun to the catalog

src/content/tools/duckrun.md

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+---
+title: duckrun
+description: Python library that runs SQL in DuckDB and reads and writes Delta Lake tables on OneLake, locally, or in cloud object storage, with a dbt adapter that materializes models as Delta tables.
+url: https://github.com/djouallah/duckrun
+docs_url: https://djouallah.github.io/duckrun/
+category: data-engineering
+type: library
+tags: [duckdb, delta, dbt, onelake, notebooks, community]
+pricing: free
+source: open-source
+maintainer: community
+author: Mimoune Djouallah
+language: Python
+status: ga
+added: 2026-09-25
+---
+
+## What it does
+
+duckrun is a thin Python layer that combines DuckDB as the query engine with delta-rs for reading and writing Delta Lake tables. It works against a local path, OneLake, S3, GCS, or ADLS, and is designed to run inside a Microsoft Fabric notebook or on a developer machine. The library exposes two ways of working: a `connect()` helper for querying and writing Delta tables straight from SQL in a notebook, and a dbt adapter built on dbt-duckdb that materializes `table` and `incremental` models as Delta tables in a Lakehouse.
+
+Writes are opt-in (`read_only=False`) and every write is snapshot-pinned, so concurrent writers fail loudly rather than silently interleaving. Plain DML such as `INSERT`, `UPDATE`, `DELETE`, and `MERGE` routes to delta-rs, and Delta time travel is available through `delta_scan()` with a version parameter.
+
+## Why use it
+
+Use it when you want a lightweight, single-process SQL engine over Lakehouse Delta tables without starting a Spark session, for example for exploration, small to medium transformations, or local development against OneLake. Multiple Lakehouses and Warehouses can be attached to one connection and joined by three-part name, which makes it practical for cross-Lakehouse queries and for Bronze, Silver, and Gold medallion layouts. The dbt adapter lets a dbt project target one or more Fabric Lakehouses as write roots.
+
+## Getting started
+
+Install from PyPI. In a Fabric notebook, upgrade and restart the kernel, since duckrun needs a newer DuckDB than the bundled build:
+
+```python
+!pip install duckrun --upgrade
+notebookutils.session.restartPython()
+```
+
+Then connect to a Lakehouse and query it:
+
+```python
+import duckrun
+
+conn = duckrun.connect("abfss://<workspace_id>@onelake.dfs.fabric.microsoft.com/<lakehouse_id>/Tables/dbo")
+conn.sql("SHOW TABLES").show()
+```
+
+For the dbt adapter, install the extra with `pip install "duckrun[dbt]"` and point a profile of type `duckrun` at a Lakehouse `Tables` path. Requires Python 3.11 or later. Licensed under MIT; the author notes it is a personal project not affiliated with any employer or vendor.