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DP-100 > Path2.Mod1.b - Make Data Available - Creating Datastores > Flashcards

Path2.Mod1.b - Make Data Available - Creating Datastores Flashcards

1
Q

I-B AK ST, ab

Create an Azure Blob Datastore through CLI:
- Three credential types you can use to connect to a Blob
- The type value for Blob
- The full CLI command line assuming a YAML file

A

CLI requires you to run az ml datastore create --file <yaml file> that sets type: azure_blob, along with credentials provided in the yaml file:
1. Identity-based access using account_name
2. Account Key using credentials: account_key
3. SAS Tokens using credentials: sas_token

Example of the YAML file. Note the type and the credentials:

# my_blob_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json
name: blob_example
type: azure_blob
description: Datastore pointing to a blob container.
account_name: mytestblobstore
container_name: data-container
credentials:
  account_key: XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXX
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2
Q

I-B AK ST, ABDs

Create an Azure Blob Datastore through Python SDK:
- Three credential types you can use to connect to a Blob
- The Constructor type for Blob Datastores

A

my_blob_datastore.yml

Python SDK requires similar to CLI while using the AzureBlobDatastore constructor, then calling MLClient.create_or_update(datastore_instance)
1. Identity-based access using account_name
2. Account Key using credentials: AccountKeyConfiguration(account_key)
3. SAS Token using credentials: SasTokenConfiguration(sas_token)

Example of that code. Note the constructor for AzureBlobDatastore and SasTokenConfiguration:

from azure.ai.ml.entities import AzureBlobDatastore
from azure.ai.ml.entities import SasTokenConfiguration
from azure.ai.ml import MLClient

ml_client = MLClient.from_config()

store = AzureBlobDatastore(
    name="blob_sas_example",
    description="Datastore pointing to a blob container using SAS token.",
    account_name="mytestblobstore",
    container_name="data-container",
    credentials=SasTokenConfiguration(
        sas_token= "?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx"
    ),
)

ml_client.create_or_update(store)
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3
Q

I-B SP, adlg2

Create an Azure Data Lake Gen 2 Datastore through CLI:
- Two credential types you can use to connect to a Data Lake
- The type value for Data Lake Gen 2

A

my_blob_datastore.yml

CLI requires you to run az ml datastore create --file <yaml file> that sets type: azure_data_lake_gen2, along with credentials provided in the yaml file:
1. Identity-based access using account_name
2. Service Principal using credentials: tenant_id, client_id, client_secret

Example of the YAML file. Note the type and the credentials:

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen2.schema.json
name: adls_gen2_example
type: azure_data_lake_gen2
description: Datastore pointing to an Azure Data Lake Storage Gen2.
account_name: mytestdatalakegen2
filesystem: my-gen2-container
credentials:
  tenant_id: XXXXXXXX-XXXX-XXXX-XXXX-XXX
  client_id: XXXXXXXX-XXXX-XXXX-XXXX-XXXX
  client_secret: XXXXXXXXXXXXXXXXXXXXXXX
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4
Q

I-B SP, ADLG2Ds

Create an Azure Data Lake Gen 2 Datastore through Python SDK:
- Two credential types you can use to connect to a Data Lake Gen 2
- The Constructor type for Data Lake Gen 2 Datastores

A

my_blob_datastore.yml

Python SDK requires similar to CLI while using the AzureDataLakeGen2Datastore constructor, then calling MLClient.create_or_update(datastore_instance)
1. Identity-based access using account_name
2. Service Principal using credentials: ServicePrincipalCredentials(tenant_id, client_id, client_secret)

Example of that code. Note the constructor for AzureDataLakeGen2Datastore and ServicePrincipalCredentials:

from azure.ai.ml.entities import AzureDataLakeGen2Datastore
from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials

from azure.ai.ml import MLClient

ml_client = MLClient.from_config()

store = AzureDataLakeGen2Datastore(
    name="adls_gen2_example",
    description="Datastore pointing to an Azure Data Lake Storage Gen2.",
    account_name="mytestdatalakegen2",
    filesystem="my-gen2-container",
     credentials=ServicePrincipalCredentials(
        tenant_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
        client_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
        client_secret= "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
    ),
)

ml_client.create_or_update(store)
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5
Q

AK ST, af

Create an Azure Files Datastore through CLI:
- Two credential types you can use to connect to a File Store
- The type value for File Store

A

my_adls_datastore.yml

CLI requires you to run az ml datastore create --file <yaml file> that sets type: azure_file, along with credentials provided in the yaml file:
1. Account Key access using credentials: account_key
2. SAS Token using credentials: sas_token

Example of the YAML file. Note the type and the credentials:

# my_files_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureFile.schema.json
name: file_sas_example
type: azure_file
description: Datastore pointing to an Azure File Share using SAS token.
account_name: mytestfilestore
file_share_name: my-share
credentials:
  sas_token: ?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxx
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6
Q

AK ST, AFDs

Create an Azure Files Datastore through Python SDK :
- Two credential types you can use to connect to a File Store
- The Constructor type for File Datastores

A

Python SDK requires similar to CLI while using the AzureFileDatastore constructor, then calling MLClient.create_or_update(datastore_instance)
1. Account Key access using AccountKeyConfiguration(account_key)
2. SAS Token using SasTokenConfiguration(sas_token)

Example of that code. Note the constructor for AzureFileDatastore and SasTokenConfiguration:

from azure.ai.ml.entities import AzureFileDatastore
from azure.ai.ml.entities import SasTokenConfiguration
from azure.ai.ml import MLClient

ml_client = MLClient.from_config()

store = AzureFileDatastore(
    name="file_sas_example",
    description="Datastore pointing to an Azure File Share using SAS token.",
    account_name="mytestfilestore",
    file_share_name="my-share",
    credentials=SasTokenConfiguration(
        sas_token="?xx=XXXX-XX-XX&xx=xxxx&xxx=xxXXXX
    ),
)

ml_client.create_or_update(store)
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7
Q

I-B SP, adlg1

Create an Azure Data Lake Gen 1 Datastore through CLI:
- Two credential types you can use to connect to a Data Lake
- The type value for Data Lake Gen 1

A

my_blob_datastore.yml

CLI is exactly the same as Gen 2 except the type requires type: azure_data_lake_gen1. You run az ml datastore create --file <yaml file> with a YAML file that sets credentials accordingly:
1. Identity-based access based on system authentication
2. Service Principal using credentials: tenant_id, client_id, client_secret

Example of the YAML file. Note the type and the simplicity of Identity-based access:

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen1.schema.json
name: alds_gen1_credless_example
type: azure_data_lake_gen1
description: Credential-less datastore pointing to an Azure Data Lake Storage Gen1.
store_name: mytestdatalakegen1
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8
Q

I-B SP, ADLG1Ds

Create an Azure Data Lake Gen 1 Datastore through Python SDK:
- Two credential types you can use to connect to a Data Lake Gen 1
- The Constructor type for Data Lake Gen 1 Datastores

A

my_blob_datastore.yml

Python SDK is exactly the same as Gen 2 except using the AzureDataLakeGen1Datastore constructor, then calling MLClient.create_or_update(datastore_instance):
1. Identity-based access based on system authentication
2. Service Principal using credentials: ServicePrincipalCredentials(tenant_id, client_id, client_secret)

Example of that code. Note the constructor for AzureDataLakeGen1Datastore and ServicePrincipalCredentials:

from azure.ai.ml.entities import AzureDataLakeGen1Datastore
from azure.ai.ml.entities.datastore.credentials import ServicePrincipalCredentials
from azure.ai.ml import MLClient

ml_client = MLClient.from_config()

store = AzureDataLakeGen1Datastore(
    name="adls_gen1_example",
    description="Datastore pointing to an Azure Data Lake Storage Gen1.",
    store_name="mytestdatalakegen1",
    credentials=ServicePrincipalCredentials(
        tenant_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
        client_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
        client_secret= "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
    ),
)

ml_client.create_or_update(store)
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9
Q

Of the four Datastores we have studied, which one does not accept Identity-Based Credentials?

A

Azure File Datastore

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10
Q

Of the four Datastores we have studied, which ones can accept Service Principal for Credentials?

A

Azure Data Lakes (Gen 1 and 2)

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11
Q

Of the four Datastores we have studied, which ones can accept Access Key and SAS Token Credentials (Credentials-based authentication)?

A

Azure Blob Datastores and Azure File Datastores

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DP-100 (58 decks)

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