---
id: dbt-fabricspark
version: "1.13.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# dbt-fabricspark — A Microsoft Fabric Spark adapter plugin for dbt

License: permissive · Maintenance: active · Downloads: 1.2M/mo

## What it is and what it does

dbt-fabricspark is a dbt adapter that bridges dbt's transformation framework to Apache Spark running in Microsoft Fabric. It connects via Livy endpoints and handles both schema-enabled and non-schema Lakehouses, automatically detecting the configuration and using appropriate naming conventions (three-part for schema-enabled, two-part otherwise). The adapter manages Livy sessions with reuse and robust retry logic, supports multiple materialization types (table, view, incremental with several strategies, seed, snapshot), and includes Fabric Environment support via environmentId configuration.

The package handles Azure authentication through multiple modes (CLI for local development, Service Principal for CI/CD, Fabric Notebook for in-notebook workflows), implements credential masking and thread-safe token refresh for security, and provides resilience through HTTP 5xx retry with exponential backoff and configurable polling timeouts. It is actively maintained, production-stable, and requires Python 3.10 or later.

Use it for:

- Transform raw data in Fabric Lakehouses using dbt SQL models as part of an ELT pipeline
- Organize and cleanse data with incremental models using merge or append strategies on Fabric Spark
- Manage schema-based data organization within a single lakehouse using auto-detected three-part naming
- Develop dbt projects locally with CLI authentication, then deploy to CI/CD with Service Principal credentials
- Build snapshots and seeds on Fabric Spark to track slowly-changing dimensions and load reference data

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

A dbt adapter that connects dbt to Apache Spark in Microsoft Fabric via Livy endpoints, enabling SQL-based data transformation workflows on Fabric Lakehouses with or without schema support.

Yes. This adapter is production-stable, actively maintained (last commit 13 days ago), has no known vulnerabilities, and low install friction. Install it if you use dbt with Microsoft Fabric Spark and need to transform data in Lakehouses. If you do not have a Fabric workspace or are not using dbt, it is not applicable.

## Install

pip install dbt-fabricspark
uv add dbt-fabricspark
poetry add dbt-fabricspark

## Installing dbt-fabricspark

Before you install: Low friction: pure Python wheel with no compiled dependencies. Active maintenance—last commit 13 days ago, production-stable status. Requires dbt-core and Azure authentication libraries (azure-core, azure-identity), all standard packages.

License in practice: MIT license (permissive). You can use, modify, and distribute this adapter freely in commercial and open-source projects with minimal restrictions.

Quickstart:

pip install dbt-fabricspark

# In profiles.yml:
fabric-spark-test:
  target: fabricspark-dev
  outputs:
    fabricspark-dev:
      type: fabricspark
      method: livy
      endpoint: https://api.fabric.microsoft.com/v1
      workspaceid: <workspace-id>
      lakehouseid: <lakehouse-id>
      lakehouse: my_lakehouse
      schema: my_lakehouse
      authentication: CLI
      threads: 1

# Then: dbt run

Requires Microsoft Fabric workspace and lakehouse; Azure CLI authentication (CLI mode) needs optional azure-cli extra. Service Principal (SPN) authentication does not require it.

Verify before relying:

- Performance characteristics and typical query latency on Fabric Spark clusters
- Supported incremental materialization strategies beyond those listed (append, merge, insert_overwrite, microbatch, delete+insert)
- Compatibility with dbt Cloud vs. dbt Core-only workflows
- Session reuse behavior and overhead reduction in production CI/CD pipelines

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dbt adapter microsoft fabric spark, fabric lakehouse dbt transformation, spark livy dbt connector, dbt fabric data pipeline, microsoft fabric data modeling, lakehouse dbt sql models, fabric spark elt adapter, dbt-adapter, fabric-spark, data-transformation

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