> For the complete documentation index, see [llms.txt](https://documentation.immuta.com/2024.3/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.immuta.com/2024.3/integrations/databricks-spark/reference-guides/configuration-settings/cluster-policies/python-sql.md).

# Python & SQL

{% hint style="info" %}
**Performance**: This is the most performant policy configuration.
{% endhint %}

In this configuration, Immuta is able to rely on Databricks-native security controls, reducing overhead. The key security control here is the enablement of process isolation. This prevents users from obtaining unintentional access to the queries of other users. In other words, masked and filtered data is consistently made accessible to users in accordance with their assigned attributes. This Immuta cluster configuration relies on Py4J security being enabled.

Many Python ML classes (such as `LogisticRegression`, `StringIndexer`, and `DecisionTreeClassifier`) and dbutils.fs are unfortunately not supported with Py4J security enabled. Users will also be [unable](https://docs.databricks.com/dev-tools/databricks-connect.html#limitations) to use the Databricks Connect client library. Additionally, only Python and SQL are available as supported languages.

For full details on Databricks’ best practices in configuring clusters, read their [governance documentation](https://docs.databricks.com/security/data-governance.html).


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