dbt-fabricspark
A Microsoft Fabric Spark adapter plugin for dbt
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Microsoft Fabric workspace and lakehouse; Azure CLI authentication (CLI mode) needs optional azure-cli extra.
- Service Principal (SPN) authentication does not require it.
- Low friction: pure Python wheel with no compiled dependencies.
License · maintenance · safety
MIT (permissive) — MIT license (permissive). You can use, modify, and distribute this adapter freely in commercial and open-source projects with minimal restrictions.
last release 2026-08-01 (13 days) · last repo commit 2026-08-09 · 64 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,236,220 downloads/mo, #4,174 on PyPI
Alternatives
Verify before relying
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- 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
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 on this page. Verify before relying on it.
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
dbt-fabricspark on PyPI
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.
Requires Microsoft Fabric workspace and lakehouse; Azure CLI authentication (CLI mode) needs optional azure-cli extra. Service Principal (SPN) authentication does not require it.
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 runVerify 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 the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesazure-coreazure-identitydbt-adaptersdbt-commondbt-corerequests |
| Maintenance | Actively maintained 13 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,236,220 / month, #4,174 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: dbt_fabricspark-1.13.0-py3-none-any.whl
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See also dbt-fabric · dbt-bigquery · dbt-snowflake · dbt-spark · dbt-databricks · semantic-link-sempy · dbt-postgres · semantic-link · dbt-vertica · dbt-sl-sdk