Metadata-Version: 2.1
Name: azure-databricks-sdk-python
Version: 0.0.4.dev1
Summary: A Python SDK for the Azure Databricks REST API 2.0.
Home-page: http://github.com/aminekaabachi/azure-databricks-sdk-python
Author: Amine Kaabachi
Author-email: ping@kaabachi.io
Maintainer: Amine Kaabachi
Maintainer-email: ping@kaabachi.io
License: MIT
Keywords: azure,databricks
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS
Requires-Dist: requests (==2.24.0)
Requires-Dist: cattrs

# Azure Databricks SDK Python

> **Warning**
> This project has been archived and is no longer actively maintained or supported. We highly recommend migrating to the official Databricks SDK for Python available at https://github.com/databricks/databricks-sdk-py.

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**azure-databricks-sdk-python** is a Python SDK for the [`Azure Databricks REST API 2.0`](https://docs.microsoft.com/en-gb/azure/databricks/dev-tools/api/latest/).

---

Easily, perform all the operations as if on the Databricks UI:

```python
from azure_databricks_sdk_python import Client
from azure_databricks_sdk_python.types.clusters import AutoScale, ClusterAttributes

client = Client(databricks_instance="<instance>", personal_access_token="<token>")
spark_conf = {'spark.speculation': True}
autoscale = AutoScale(min_workers=0, max_workers=1)
attributes = ClusterAttributes(cluster_name="my-cluster",
                            spark_version="7.2.x-scala2.12",
                            node_type_id="Standard_D3_v2",
                            spark_conf=spark_conf,
                            autoscale=autoscale)
created = client.clusters.create(attributes)
print(created.cluster_id)
```

## Beloved Features

**azure-databricks-sdk-python** is ready for your use-case:

- Clear standard to access to APIs.
- Contains custom types for the API results and requests.
- Support for Personal Access token authentification.
- Support for Azure AD authentification.
- Support for the use of Azure AD service principals.
- Allows free-style API calls with a force mode -(bypass types validation).
- Error handeling and proxy support.

Officially supports 3.6+, and runs great on PyPy.

## Implementation Progress

Please refer to the progress below:

| Feature          | Progress |
| :--------------- | :------: |
| Authentification |  100% ✔  |
| Custom types     |   25%    |
| API Wrappers     |   25%    |
| Error handling   |   80%    |
| Proxy support    |    0%    |
| Documentation    |   20%    |

As for specific API wrappers:

| API                   | Progress |
| :-------------------- | :------: |
| Clusters API          |  100% ✔  |
| Secrets API           |  100% ✔  |
| Token API             |  100% ✔  |
| Jobs API              |    0%    |
| DBFS API              |    0%    |
| Groups API            |    0%    |
| Libraries API         |    0%    |
| Workspace API         |    0%    |
| Clusters Policies API |    0%    |
| Instance Pools API    |    0%    |
| MLflow API            |    0%    |
| Permissions API       |    0%    |
| SCIM API              |    0%    |
| Token Management API  |    0%    |

## Documentation

Check the documentation on [readthedocs.org](https://azure-databricks-sdk-python.readthedocs.io/en/latest/).

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