pulumi-azure-native-kotlin/com.pulumi.azurenative.machinelearningservices.kotlin/Datastore

Datastore

class Datastore : KotlinCustomResource

Azure Resource Manager resource envelope. Azure REST API version: 2023-04-01. Other available API versions: 2021-03-01-preview, 2022-02-01-preview, 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01.

Example Usage

CreateOrUpdate datastore (Azure Data Lake Gen1 w/ ServicePrincipal).

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var datastore = new AzureNative.MachineLearningServices.Datastore("datastore", new()
    {
        DatastoreProperties = new AzureNative.MachineLearningServices.Inputs.AzureDataLakeGen1DatastoreArgs
        {
            Credentials = new AzureNative.MachineLearningServices.Inputs.ServicePrincipalDatastoreCredentialsArgs
            {
                AuthorityUrl = "string",
                ClientId = "00000000-1111-2222-3333-444444444444",
                CredentialsType = "ServicePrincipal",
                ResourceUrl = "string",
                Secrets = new AzureNative.MachineLearningServices.Inputs.ServicePrincipalDatastoreSecretsArgs
                {
                    ClientSecret = "string",
                    SecretsType = "ServicePrincipal",
                },
                TenantId = "00000000-1111-2222-3333-444444444444",
            },
            DatastoreType = "AzureDataLakeGen1",
            Description = "string",
            StoreName = "string",
            Tags =
            {
                { "string", "string" },
            },
        },
        Name = "string",
        ResourceGroupName = "test-rg",
        SkipValidation = false,
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	"github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewDatastore(ctx, "datastore", &machinelearningservices.DatastoreArgs{
			DatastoreProperties: machinelearningservices.AzureDataLakeGen1Datastore{
				Credentials: machinelearningservices.ServicePrincipalDatastoreCredentials{
					AuthorityUrl:    "string",
					ClientId:        "00000000-1111-2222-3333-444444444444",
					CredentialsType: "ServicePrincipal",
					ResourceUrl:     "string",
					Secrets: machinelearningservices.ServicePrincipalDatastoreSecrets{
						ClientSecret: "string",
						SecretsType:  "ServicePrincipal",
					},
					TenantId: "00000000-1111-2222-3333-444444444444",
				},
				DatastoreType: "AzureDataLakeGen1",
				Description:   "string",
				StoreName:     "string",
				Tags: map[string]interface{}{
					"string": "string",
				},
			},
			Name:              pulumi.String("string"),
			ResourceGroupName: pulumi.String("test-rg"),
			SkipValidation:    pulumi.Bool(false),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Datastore;
import com.pulumi.azurenative.machinelearningservices.DatastoreArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var datastore = new Datastore("datastore", DatastoreArgs.builder()
            .datastoreProperties(Map.ofEntries(
                Map.entry("credentials", Map.ofEntries(
                    Map.entry("authorityUrl", "string"),
                    Map.entry("clientId", "00000000-1111-2222-3333-444444444444"),
                    Map.entry("credentialsType", "ServicePrincipal"),
                    Map.entry("resourceUrl", "string"),
                    Map.entry("secrets", Map.ofEntries(
                        Map.entry("clientSecret", "string"),
                        Map.entry("secretsType", "ServicePrincipal")
                    )),
                    Map.entry("tenantId", "00000000-1111-2222-3333-444444444444")
                )),
                Map.entry("datastoreType", "AzureDataLakeGen1"),
                Map.entry("description", "string"),
                Map.entry("storeName", "string"),
                Map.entry("tags", AzureBlobDatastoreArgs.builder()
                    .string("string")
                    .build())
            ))
            .name("string")
            .resourceGroupName("test-rg")
            .skipValidation(false)
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate datastore (Azure Data Lake Gen2 w/ Service Principal).

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var datastore = new AzureNative.MachineLearningServices.Datastore("datastore", new()
    {
        DatastoreProperties = new AzureNative.MachineLearningServices.Inputs.AzureDataLakeGen2DatastoreArgs
        {
            AccountName = "string",
            Credentials = new AzureNative.MachineLearningServices.Inputs.ServicePrincipalDatastoreCredentialsArgs
            {
                AuthorityUrl = "string",
                ClientId = "00000000-1111-2222-3333-444444444444",
                CredentialsType = "ServicePrincipal",
                ResourceUrl = "string",
                Secrets = new AzureNative.MachineLearningServices.Inputs.ServicePrincipalDatastoreSecretsArgs
                {
                    ClientSecret = "string",
                    SecretsType = "ServicePrincipal",
                },
                TenantId = "00000000-1111-2222-3333-444444444444",
            },
            DatastoreType = "AzureDataLakeGen2",
            Description = "string",
            Endpoint = "string",
            Filesystem = "string",
            Protocol = "string",
            Tags =
            {
                { "string", "string" },
            },
        },
        Name = "string",
        ResourceGroupName = "test-rg",
        SkipValidation = false,
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	"github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewDatastore(ctx, "datastore", &machinelearningservices.DatastoreArgs{
			DatastoreProperties: machinelearningservices.AzureDataLakeGen2Datastore{
				AccountName: "string",
				Credentials: machinelearningservices.ServicePrincipalDatastoreCredentials{
					AuthorityUrl:    "string",
					ClientId:        "00000000-1111-2222-3333-444444444444",
					CredentialsType: "ServicePrincipal",
					ResourceUrl:     "string",
					Secrets: machinelearningservices.ServicePrincipalDatastoreSecrets{
						ClientSecret: "string",
						SecretsType:  "ServicePrincipal",
					},
					TenantId: "00000000-1111-2222-3333-444444444444",
				},
				DatastoreType: "AzureDataLakeGen2",
				Description:   "string",
				Endpoint:      "string",
				Filesystem:    "string",
				Protocol:      "string",
				Tags: map[string]interface{}{
					"string": "string",
				},
			},
			Name:              pulumi.String("string"),
			ResourceGroupName: pulumi.String("test-rg"),
			SkipValidation:    pulumi.Bool(false),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Datastore;
import com.pulumi.azurenative.machinelearningservices.DatastoreArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var datastore = new Datastore("datastore", DatastoreArgs.builder()
            .datastoreProperties(Map.ofEntries(
                Map.entry("accountName", "string"),
                Map.entry("credentials", Map.ofEntries(
                    Map.entry("authorityUrl", "string"),
                    Map.entry("clientId", "00000000-1111-2222-3333-444444444444"),
                    Map.entry("credentialsType", "ServicePrincipal"),
                    Map.entry("resourceUrl", "string"),
                    Map.entry("secrets", Map.ofEntries(
                        Map.entry("clientSecret", "string"),
                        Map.entry("secretsType", "ServicePrincipal")
                    )),
                    Map.entry("tenantId", "00000000-1111-2222-3333-444444444444")
                )),
                Map.entry("datastoreType", "AzureDataLakeGen2"),
                Map.entry("description", "string"),
                Map.entry("endpoint", "string"),
                Map.entry("filesystem", "string"),
                Map.entry("protocol", "string"),
                Map.entry("tags", AzureBlobDatastoreArgs.builder()
                    .string("string")
                    .build())
            ))
            .name("string")
            .resourceGroupName("test-rg")
            .skipValidation(false)
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate datastore (Azure File store w/ AccountKey).

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var datastore = new AzureNative.MachineLearningServices.Datastore("datastore", new()
    {
        DatastoreProperties = new AzureNative.MachineLearningServices.Inputs.AzureFileDatastoreArgs
        {
            AccountName = "string",
            Credentials = new AzureNative.MachineLearningServices.Inputs.AccountKeyDatastoreCredentialsArgs
            {
                CredentialsType = "AccountKey",
                Secrets = new AzureNative.MachineLearningServices.Inputs.AccountKeyDatastoreSecretsArgs
                {
                    Key = "string",
                    SecretsType = "AccountKey",
                },
            },
            DatastoreType = "AzureFile",
            Description = "string",
            Endpoint = "string",
            FileShareName = "string",
            Protocol = "string",
            Tags =
            {
                { "string", "string" },
            },
        },
        Name = "string",
        ResourceGroupName = "test-rg",
        SkipValidation = false,
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	"github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewDatastore(ctx, "datastore", &machinelearningservices.DatastoreArgs{
			DatastoreProperties: machinelearningservices.AzureFileDatastore{
				AccountName: "string",
				Credentials: machinelearningservices.AccountKeyDatastoreCredentials{
					CredentialsType: "AccountKey",
					Secrets: machinelearningservices.AccountKeyDatastoreSecrets{
						Key:         "string",
						SecretsType: "AccountKey",
					},
				},
				DatastoreType: "AzureFile",
				Description:   "string",
				Endpoint:      "string",
				FileShareName: "string",
				Protocol:      "string",
				Tags: map[string]interface{}{
					"string": "string",
				},
			},
			Name:              pulumi.String("string"),
			ResourceGroupName: pulumi.String("test-rg"),
			SkipValidation:    pulumi.Bool(false),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Datastore;
import com.pulumi.azurenative.machinelearningservices.DatastoreArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var datastore = new Datastore("datastore", DatastoreArgs.builder()
            .datastoreProperties(Map.ofEntries(
                Map.entry("accountName", "string"),
                Map.entry("credentials", Map.ofEntries(
                    Map.entry("credentialsType", "AccountKey"),
                    Map.entry("secrets", Map.ofEntries(
                        Map.entry("key", "string"),
                        Map.entry("secretsType", "AccountKey")
                    ))
                )),
                Map.entry("datastoreType", "AzureFile"),
                Map.entry("description", "string"),
                Map.entry("endpoint", "string"),
                Map.entry("fileShareName", "string"),
                Map.entry("protocol", "string"),
                Map.entry("tags", AzureBlobDatastoreArgs.builder()
                    .string("string")
                    .build())
            ))
            .name("string")
            .resourceGroupName("test-rg")
            .skipValidation(false)
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate datastore (AzureBlob w/ AccountKey).

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var datastore = new AzureNative.MachineLearningServices.Datastore("datastore", new()
    {
        DatastoreProperties = new AzureNative.MachineLearningServices.Inputs.AzureBlobDatastoreArgs
        {
            AccountName = "string",
            ContainerName = "string",
            Credentials = new AzureNative.MachineLearningServices.Inputs.AccountKeyDatastoreCredentialsArgs
            {
                CredentialsType = "AccountKey",
                Secrets = new AzureNative.MachineLearningServices.Inputs.AccountKeyDatastoreSecretsArgs
                {
                    Key = "string",
                    SecretsType = "AccountKey",
                },
            },
            DatastoreType = "AzureBlob",
            Description = "string",
            Endpoint = "core.windows.net",
            Protocol = "https",
            Tags =
            {
                { "string", "string" },
            },
        },
        Name = "string",
        ResourceGroupName = "test-rg",
        SkipValidation = false,
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	"github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewDatastore(ctx, "datastore", &machinelearningservices.DatastoreArgs{
			DatastoreProperties: machinelearningservices.AzureBlobDatastore{
				AccountName:   "string",
				ContainerName: "string",
				Credentials: machinelearningservices.AccountKeyDatastoreCredentials{
					CredentialsType: "AccountKey",
					Secrets: machinelearningservices.AccountKeyDatastoreSecrets{
						Key:         "string",
						SecretsType: "AccountKey",
					},
				},
				DatastoreType: "AzureBlob",
				Description:   "string",
				Endpoint:      "core.windows.net",
				Protocol:      "https",
				Tags: map[string]interface{}{
					"string": "string",
				},
			},
			Name:              pulumi.String("string"),
			ResourceGroupName: pulumi.String("test-rg"),
			SkipValidation:    pulumi.Bool(false),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Datastore;
import com.pulumi.azurenative.machinelearningservices.DatastoreArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var datastore = new Datastore("datastore", DatastoreArgs.builder()
            .datastoreProperties(Map.ofEntries(
                Map.entry("accountName", "string"),
                Map.entry("containerName", "string"),
                Map.entry("credentials", Map.ofEntries(
                    Map.entry("credentialsType", "AccountKey"),
                    Map.entry("secrets", Map.ofEntries(
                        Map.entry("key", "string"),
                        Map.entry("secretsType", "AccountKey")
                    ))
                )),
                Map.entry("datastoreType", "AzureBlob"),
                Map.entry("description", "string"),
                Map.entry("endpoint", "core.windows.net"),
                Map.entry("protocol", "https"),
                Map.entry("tags", AzureBlobDatastoreArgs.builder()
                    .string("string")
                    .build())
            ))
            .name("string")
            .resourceGroupName("test-rg")
            .skipValidation(false)
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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Import

An existing resource can be imported using its type token, name, and identifier, e.g.

$ pulumi import azure-native:machinelearningservices:Datastore string /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}
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Properties

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val datastoreProperties: Output<Any>

Required Additional attributes of the entity.

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val id: Output<String>
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val name: Output<String>

The name of the resource

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val pulumiChildResources: Set<KotlinResource>
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val systemData: Output<SystemDataResponse>

Azure Resource Manager metadata containing createdBy and modifiedBy information.

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val type: Output<String>

The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"

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val urn: Output<String>