> For the complete documentation index, see [llms.txt](https://help.protecto.ai/getting-started/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.protecto.ai/getting-started/protecto-overview/introduction/add-new-data-source/databricks/steps-to-create-azure-databricks-cluster.md).

# Steps to create Azure Databricks Cluster

1. Login to Azure Databricks workspace.
2. Select Data Science & Engineering from sidebar.
3. Select create -> New cluster. Add below details:
   * Policy - Unrestricted
   * Cluster name - protecto
   * Cluster mode - Standard
   * Databricks runtime version - latest with LTS
   * Enable table access control and only allow Python and SQL commands
   * Worker type - Node Size - 4 Core, 14 GB RAM (Standard\_DS3\_v2)
   * Driver Type - same as worker
   * In Advance option

 Add the following in spark config:

             spark.databricks.acl.dfAclsEnabled true

             spark data brickss.repl.allowedLanguages python,sql

             spark.databricks.delta.preview\.enabled true

Note : python notebook and job creation steps will be shared during Protecto product installation. Please find the attached files (protecto\_python\_notebook).

{% file src="/files/OeogiUw30rdt4m2DPOrO" %}

**Credentials needed to connect Databricks:**

* Service principal application id (client id)
* Service principal directory id (tenant id)
* Service principal application secret (client secret)
* Server hostname
* Port
* Sql endpoint http path
* Catalog name (eg: hive\_metastore)