Azure Databricks REST API Authentication and Authorization Guide

Azure Databricks REST API offers two types of authentication: Azure Active Directory (AAD) and Personal Access Token (PAT).

To use AAD authentication, you need to register your application in the Azure portal and create a client ID and client secret.

The client ID is used as a username and the client secret is used as a password to authenticate with the Databricks REST API.

AAD authentication is the recommended method for authentication with the Databricks REST API.

To use PAT authentication, you need to create a personal access token in the Databricks UI.

The PAT can be used as a username and password to authenticate with the Databricks REST API.

PAT authentication is suitable for scripts and applications that need to authenticate with the Databricks REST API.

The Databricks REST API uses the OAuth 2.0 authorization framework to authenticate and authorize requests.

The authorization token is valid for 30 days and can be refreshed using the refresh token.

The refresh token can be obtained by making a POST request to the Databricks REST API with the client ID and client secret.

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Authentication is a crucial aspect of working with the Azure Databricks REST API. You can select from various authentication methods, including OAuth for service principals (OAuth M2M), OAuth for users (OAuth U2M), and Personal Access Tokens (PAT).

For user account authentication, Azure Databricks OAuth is handled for you with Databricks client unified authentication, as long as the tools and SDKs implement it. This simplifies the process and provides a more secure way to authenticate.

There are two main options for authenticating a Databricks CLI command or API call: using an Azure Databricks user account (U2M) or an Azure Databricks service principal (M2M). Choose U2M when running commands from your local client environment, and M2M when others will be running your code or for automation.

Here are the authentication methods for service principals:

For service principal authentication, you need to provide client credentials, including a client ID and client secret. These are used to authenticate with the Azure Databricks API. You can set these environment variables directly or through a Databricks configuration profile (.databrickscfg) on your client machine.

To use an OAuth access token, your Azure Databricks workspace or account administrator must have granted your user account or service principal the CAN USE privilege for the account and workspace features your code will access.

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Authorization is a crucial aspect of working with the Azure Databricks REST API. You'll need to authenticate and authorize your requests to access the resources you need.

To authenticate, you can use one of several methods, including OAuth for service principals (M2M), OAuth for users (U2M), personal access tokens (PAT), Azure managed identities authentication, and Microsoft Entra ID service principal authentication.

You can choose the best authentication method for your use case, such as unattended authentication scenarios, attended authentication scenarios, or scenarios where your target tool doesn't support OAuth.

Here are the different authentication methods and their use cases:

Once you've chosen an authentication method, you'll need to set up the necessary environment variables to authenticate with Azure Databricks. For user account (U2M) authentication, you can use Databricks client unified authentication, which handles OAuth for you. For service principal (M2M) authentication, you'll need to provide client credentials, including a client ID and client secret.

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You can set these environment variables directly or through the use of a Databricks configuration profile (.databrickscfg) on your client machine. Make sure to have an access token linked to the account you'll use to call the Databricks API, and that your Azure Databricks workspace or account administrator has granted your user account or service principal the CAN USE privilege for the account and workspace features your code will access.

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APIs are a crucial part of interacting with Azure Databricks. They provide a standardized way to access and manipulate data.

To use the Azure Databricks REST API, you need to authenticate your API calls. This is done by providing an access token, which can be obtained from the Azure portal.

Authentication is a critical step in using the API, as it ensures that only authorized users can access and modify data. Azure Databricks uses OAuth 2.0 for authentication, which provides a secure way to access resources.

The Azure Databricks REST API is designed to be used programmatically, making it easy to integrate into your existing workflows and tools.

Authorization is a crucial aspect of API security. It determines who can access your API and what actions they can perform.

To authorize your API, you can use Azure Active Directory (AAD) or Azure Databricks' own authentication mechanisms. For example, you can use OAuth for service principals (OAuth M2M) for unattended authentication scenarios.

Azure Databricks also provides unified client authentication to make authentication easier and more secure. This involves setting specific environment variables such as DATABRICKS_HOST, DATABRICKS_ACCOUNT_ID, DATABRICKS_CLIENT_ID, and DATABRICKS_CLIENT_SECRET.

These environment variables can be set directly or through the use of a Databricks configuration profile (.databrickscfg) on your client machine. However, you must ensure that your Azure Databricks workspace or account administrator has granted your user account or service principal the CAN USE privilege for the account and workspace features your code will access.

Here are some common authentication methods for Azure Databricks:

If you are writing code that accesses third-party services, tools, or SDKs, you must use the authentication and authorization mechanisms provided by the third-party. However, if you must grant a third-party tool, SDK, or service access to your Azure Databricks account or workspace resources, Databricks provides support for tools like Terraform, Git providers, Jenkins, and Azure DevOps.

Responses can vary depending on the outcome of your API call. A successful response is typically indicated by a 200 OK status code.

The type of response can be a Workspace, which provides information about the workspace, including provisioning status. This is often returned in response to a successful request to create a new workspace.

You can also receive a WorkspaceListResult, which is an array of workspaces. This type of response is returned when you're retrieving a list of workspaces.

Other status codes, such as 201 Created, can also be returned, indicating that a workspace was successfully created. However, if an error occurs, you'll receive an ErrorResponse, which provides a description of the error.

Here's a breakdown of the different response types:

A 202 Accepted status code can also be returned, indicating that a request was accepted but has not yet been processed.

An Access Connector is a resource associated with a Databricks Workspace.

The resource ID of an Access Connector is a unique string.

The identity type of an Access Connector can be either 'UserAssigned' or 'SystemAssigned'.

To use a UserAssigned identity, you need to provide the resource ID of the User Assigned Identity associated with the Access Connector.

For a SystemAssigned identity, you don't need to provide a user-assigned identity ID.

Here's a breakdown of the required fields for an Access Connector:

API Definitions provide a clear understanding of the types of variables used in Azure Databricks REST API. The CustomParameterType is the type of variable that this is.

In the API Definitions, you'll find various types of variables, including CustomParameterType, which is used to specify the type of variable. This type can be used to define the type of variable in various contexts throughout the API.

Here are some key types of variables found in API Definitions:

These definitions provide a solid foundation for understanding the API and how to work with variables in a clear and efficient manner.

API definitions are the backbone of any API, providing a clear understanding of the data and functionality being exposed. In this section, we'll explore the various definitions that make up the API.

A key concept in API definitions is the type of variable, which can be a Bool, Object, or String. This is crucial in determining how data is stored and retrieved.

One of the most important definitions is EncryptionEntitiesDefinition, which outlines the encryption properties for Databricks workspace resources. This includes ManagedDiskEncryption and EncryptionV2, which provide encryption properties for managed disks and managed services, respectively.

EncryptionEntitiesDefinition is a critical component of API definitions, ensuring that sensitive data is protected and secure.

EncryptionEntitiesDefinition has two key components: managedDisk and managedServices. ManagedDiskEncryption provides encryption properties for managed disks, while EncryptionV2 provides encryption properties for managed services.

Here is a summary of the key definitions:

These definitions are essential in building a robust and secure API, and are critical in ensuring that sensitive data is protected.

Cluster updates are an essential aspect of maintaining a cluster's health and performance. The AutomaticClusterUpdateDefinition feature is used to determine the status of automated cluster updates.

This feature is crucial for ensuring that clusters receive the necessary updates to function optimally.

Error handling is a crucial aspect of working with the Azure Databricks REST API. You'll need to be able to identify and understand error messages.

The API returns an error object with a specific structure. This object contains three key pieces of information: code, message, and target.

Here's a breakdown of what each of these fields represents:

These fields provide a clear and concise way to understand the nature of an error and how to resolve it.

You can use the Encryption object to manage encryption for your workspace. The object contains details of the encryption used, including the key name, key source, and key vault URI.

The key name is a required field, and it should be the name of the KeyVault key.

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Encryption keys can come from either the default source or Microsoft.Keyvault.

To specify the key source, you can use the keySource field, which accepts the values "Default" or "Microsoft.Keyvault" (case-insensitive).

If you're using a KeyVault key, you'll also need to specify the key vault URI and version.

Here's a summary of the Encryption object fields:

Endpoints are a crucial part of Azure Databricks REST API.

A private endpoint is a network interface that connects a virtual network to Azure services.

Private endpoint connections are used to enable private access to Azure Databricks.

The private endpoint property of a private endpoint connection is a key component of this process.

You can use private endpoint connections to connect your virtual network to Azure Databricks, allowing for private access to the service.

Private endpoint connections are useful for scenarios where you need to access Azure Databricks from a virtual network.

When creating a workspace in Azure Databricks, you have the option to specify default catalog properties. This is where things get interesting.

The initialName property is a string that specifies the initial name of the default catalog. If you don't specify a value, the name of the workspace will be used instead.

You can define the initial type of the default catalog using the initialType property. This property has two possible values: HiveMetastore and UnityCatalog. The default value is HiveMetastore.

Here's a summary of the properties you can use to specify default catalog properties:

Default Catalog Properties are crucial for setting up your workspace, and understanding them can save you a lot of hassle down the line.

When creating a default catalog, you can specify the initial Name, which will be used if you don't provide one. This Name will be the same as the workspace name if left blank.

The initial Type of the default catalog is also important, as it defines how the catalog will be used. You can choose between HiveMetastore or UnityCatalog, and the default value is HiveMetastore.

The resource identifier, or ID, is also a property of the default catalog. This is a unique string that identifies the resource.

Here's a quick rundown of the default catalog properties:

System Data is a crucial aspect of data management, and it's essential to understand what it entails. System Data provides metadata about the creation and last modification of a resource.

The timestamp of resource creation is recorded as a string in UTC format. This timestamp is represented by the createdAt field. I've seen this field come in handy when tracking the history of changes made to a resource.

The identity that created the resource is stored in the createdBy field, which is also a string. This information is useful for auditing purposes, such as identifying who created a particular resource.

The type of identity that created the resource is indicated by the createdByType field, which can take on various values. This field provides additional context about the creator's identity.

The timestamp of the last modification is recorded in the lastModifiedAt field, which is also a string in UTC format. This field helps track when changes were made to a resource.

The identity that last modified the resource is stored in the lastModifiedBy field, which is a string. This information is useful for auditing purposes, such as identifying who made recent changes to a resource.

The type of identity that last modified the resource is indicated by the lastModifiedByType field, which can take on various values. This field provides additional context about the last modifier's identity.

Here's a quick rundown of the System Data fields:

Properties and Parameters are crucial when working with Azure Databricks REST API. You can specify custom parameters for cluster creation, which include the ID of an Azure Machine Learning workspace to link with Databricks workspace.

The custom parameters also include the name of a public subnet within the virtual network, and the ID of a virtual network where the Databricks cluster should be created. Additionally, you can enable no public IP, prepare the workspace for encryption, and specify the storage account name and SKU name.

Here are some key parameters to consider:

Encryption properties are also crucial, and you can specify the encryption entities definition for the workspace. This includes the type and value of the encryption.

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URI parameters play a crucial role in defining the specifics of a request. There are several parameters that can be used to customize a URI.

The name of the resource group is a required parameter, and it's case insensitive. It's a string value that must be provided.

The ID of the target subscription is another required parameter, and it must be a UUID. This ensures that the subscription is properly identified.

The name of the workspace is also a required parameter, and it's a string value. This helps to identify the specific workspace being referenced.

The API version is a required parameter, and it's a string value. This determines the version of the API being used for the operation.

Here are the URI parameters in a table format for easy reference:

The Request Body is a crucial part of the workspace creation process, and it's where you'll define the properties and parameters of your new workspace.

The location of your workspace is a required field, and it must be a string that specifies the geo-location where the resource lives.

The managed resource group ID is also a required field, and it must be a string that specifies the managed resource group ID.

You can associate an access connector with your workspace, which will allow you to access the workspace securely.

The workspace provider authorizations are another important property, and they're an array of workspace provider authorization objects.

The created by property indicates the Object ID, PUID, and Application ID of the entity that created the workspace.

The default catalog properties define the configuration for the default catalog during workspace creation.

The default storage firewall configuration information is also defined in the request body.

Encryption properties for the workspace are defined in the encryption property, which is an object that contains the encryption details.

The enhanced security compliance definition contains settings related to the Enhanced Security and Compliance Add-On.

The managed disk identity and storage account identity are also defined in the request body.

The public network access type for accessing the workspace is defined in the public network access property, and it can be set to disabled to access the workspace only via private link.

The required NSG rules are also defined in the request body, and they specify whether data plane to control plane communication happens over private endpoint.

The UI definition URI is a string that specifies the blob URI where the UI definition file is located.

The updated by property indicates the Object ID, PUID, and Application ID of the entity that last updated the workspace.

The SKU of the resource is also defined in the request body, and it specifies the type of resource being created.

Resource tags are also defined in the request body, and they're an object that contains the tags applied to the resource.

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Here's a summary of the request body properties and their types:

Custom Parameters are used for Cluster Creation in Databricks. They allow you to specify various settings, such as the ID of an Azure Machine Learning workspace to link with Databricks workspace.

You can specify the ID of a Virtual Network where the Databricks Cluster should be created using the customVirtualNetworkId parameter.

The enableNoPublicIp parameter is a boolean indicating whether the public IP should be disabled, with a default value of true.

Custom parameters like encryption and storageAccountName can be used to specify encryption details and default DBFS storage account name, respectively.

The type of encryption used can be specified using the WorkspaceEncryptionParameter, which contains the encryption details for Customer-Managed Key (CMK) enabled workspace.

Here's a summary of some common custom parameters:

The requireInfrastructureEncryption parameter is a boolean indicating whether or not the DBFS root file system will be enabled with secondary layer of encryption with platform managed keys for data at rest.

Properties are a crucial aspect of Databricks workspaces. They allow you to specify default catalog properties during workspace creation, such as the initial name and type of the default catalog.

The initial name of the default catalog can be specified using the `initialName` property, which is a string. If not specified, the name of the workspace will be used.

Default catalog properties also include the `initialType` property, which defines the initial type of the default catalog. Possible values for `initialType` are HiveMetastore and UnityCatalog.

Encryption properties for the workspace can be specified using the `entities` property, which is an EncryptionEntitiesDefinition. This is used to define encryption entities for the workspace.

In addition to these properties, the workspace can also be configured with access connector, authorizations, and default storage firewall settings. These settings are optional and can be specified using the `accessConnector`, `authorizations`, and `defaultStorageFirewall` properties, respectively.

Here is a list of some of the properties that can be specified for a Databricks workspace:

These are just a few examples of the properties that can be specified for a Databricks workspace. The specific properties that are available will depend on the context in which the workspace is being created.

The initial type of the default catalog is a crucial setting during workspace creation. It defines the type of catalog that will be used by default.

You can specify the initial type as either HiveMetastore or UnityCatalog. Both options are case-insensitive, so you can enter them in any format you prefer.

The default value for the initial type is HiveMetastore. If you don't specify a different value, the workspace will use HiveMetastore by default.

Here are the possible values for the initial type: