azure-mgmt-databricks
Microsoft Azure Databricks Management Client Library for Python
Decision gist · record as of 2026-08-14
Yes. This is the official, actively maintained Azure SDK for Databricks management with no known vulnerabilities, low install friction, and permissive licensing. Install it if you need to manage Azure Databricks resources from Python code. Be aware that version 3.0.0 introduces breaking changes to model structures that may require migration if upgrading from earlier versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Azure subscription and environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET, and AZURE_SUBSCRIPTION_ID for authentication.
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with recent release (37 days old) and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most deployment scenarios.
last release 2026-07-08 (37 days) · last repo commit 2026-08-14 · 5,588 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 661,565 downloads/mo, #5,447 on PyPI
Alternatives
Verify before relying
pip install azure-mgmt-databricks
from azure.mgmt.databricks import AzureDatabricksManagementClient
from azure.identity import DefaultAzureCredential
import os
sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = AzureDatabricksManagementClient(
credential=DefaultAzureCredential(),
subscription_id=sub_id
)- Whether version 3.0.0's breaking changes to hybrid models require significant refactoring for existing codebases.
- Performance characteristics and rate limits when managing large numbers of workspaces or clusters.
- Compatibility with non-Azure cloud environments or hybrid deployments.
What it is and what it does
This is the official Azure SDK client library for managing Databricks resources in Azure. It wraps the Azure Resource Manager API to let you programmatically create, configure, and delete Databricks workspaces, clusters, access connectors, and related infrastructure. The library depends on isodate, azure-mgmt-core, and typing-extensions to handle REST communication, authentication, and type hints.
Version 3.0.0 introduces hybrid models with dual dictionary and model behavior, adds cloud_setting configuration, and expands the API surface with new resource types like AutomaticClusterUpdateDefinition and EnhancedSecurityComplianceDefinition. It requires Python 3.10 or later and authenticates via Microsoft Entra using environment variables or credential objects.
Use it for
- Automate provisioning and teardown of Databricks workspaces as part of infrastructure-as-code pipelines.
- Manage workspace access connectors and private endpoint connections for network isolation.
- Configure workspace security settings, encryption, and compliance profiles programmatically.
- Integrate Databricks resource management into Python-based DevOps or cloud orchestration tools.
- Query and monitor workspace properties, provisioning state, and system metadata.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is the official, actively maintained Azure SDK for Databricks management with no known vulnerabilities, low install friction, and permissive licensing. Install it if you need to manage Azure Databricks resources from Python code. Be aware that version 3.0.0 introduces breaking changes to model structures that may require migration if upgrading from earlier versions.
Install
azure-mgmt-databricks on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Actively maintained with recent release (37 days old) and no known vulnerabilities. Requires Python 3.10 or later.
Requires Azure subscription and environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET, and AZURE_SUBSCRIPTION_ID for authentication.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most deployment scenarios.
Quickstart
pip install azure-mgmt-databricks
from azure.mgmt.databricks import AzureDatabricksManagementClient
from azure.identity import DefaultAzureCredential
import os
sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = AzureDatabricksManagementClient(
credential=DefaultAzureCredential(),
subscription_id=sub_id
)Verify before relying
- Whether version 3.0.0's breaking changes to hybrid models require significant refactoring for existing codebases.
- Performance characteristics and rate limits when managing large numbers of workspaces or clusters.
- Compatibility with non-Azure cloud environments or hybrid deployments.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesisodateazure-mgmt-coretyping-extensions |
| Maintenance | Actively maintained 37 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 661,565 / month, #5,447 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/StableProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: azure_mgmt_databricks-3.0.0-py3-none-any.whl
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See also azure-mgmt-resource · azure-mgmt-kusto · databricks-sdk · azure-mgmt-storage · azure-mgmt-servicebus · azure-mgmt-hdinsight · azure-mgmt-machinelearningservices · dbl-sat-sdk · azure-mgmt-eventhub · azure-mgmt-datafactory