Import

To import a dataset into Data Wrangler from Athena

1. Sign into Amazon SageMaker AI Console.

2. Choose Studio.

3. Choose Launch app.

4. From the dropdown list, select Studio.

5. Choose the Home icon.

6. Choose Data.

7. Choose Data Wrangler.

8. Choose Import data.

9. Under Available, choose Amazon Athena.

10. For Data Catalog, choose a data catalog.

11. Use the Database dropdown list to select the database that you want to query. When you select a database, you can preview all tables in your database using the Tables listed under Details.

12. (Optional) Choose Advanced configuration.

    1. Choose a Workgroup.

    2. If your workgroup hasn't enforced the Amazon S3 output location or if you don't use a workgroup, specify a value for Amazon S3 location of query results.

    3. (Optional) For Data retention period, select the checkbox to set a data retention period and specify the number of days to store the data before it's deleted.

    4. (Optional) By default, Data Wrangler saves the connection. You can choose to deselect the checkbox and not save the connection.

13. For Sampling, choose a sampling method. Choose None to turn off sampling.

14. Enter your query in the query editor and use the Run button to run the query. After a successful query, you can preview your result under the editor.

    Note

    Salesforce data uses the timestamptz type. If you're querying the timestamp column that you've imported to Athena from Salesforce, cast the data in the column to the timestamp type. The following query casts the timestamp column to the correct type.

    
    # cast column timestamptz_col as timestamp type, and name it as timestamp_col
    select cast(timestamptz_col as timestamp) as timestamp_col from table
                            

15. To import the results of your query, select Import.

To give permissions to manage the lifecycle configuration do the following.

1. Sign in to the AWS Management Console and open the IAM console at https://console.aws.amazon.com/iam/.

2. Choose Roles.

3. In the search bar, specify the Amazon SageMaker AI execution role that Amazon SageMaker Studio Classic is using.

4. Choose the role.

5. Choose Add permissions.

6. Choose Create inline policy.

7. For Service, specify S3 and choose it.

8. Under the Read section, choose GetLifecycleConfiguration.

9. Under the Write section, choose PutLifecycleConfiguration.

10. For Resources, choose Specific.

11. For Actions, select the arrow icon next to Permissions management.

12. Choose PutResourcePolicy.

13. For Resources, choose Specific.

14. Choose the checkbox next to Any in this account.

15. Choose Review policy.

16. For Name, specify a name.

17. Choose Create policy.

To connect to a Amazon Redshift cluster

1. Sign into Amazon SageMaker AI Console.

2. Choose Studio.

3. Choose Launch app.

4. From the dropdown list, select Studio.

5. Choose the Home icon.

6. Choose Data.

7. Choose Data Wrangler.

8. Choose Import data.

9. Under Available, choose Amazon Athena.

10. Choose Amazon Redshift.

11. Choose Temporary credentials (IAM) for Type.

12. Enter a Connection Name. This is a name used by Data Wrangler to identify this connection.

13. Enter the Cluster Identifier to specify to which cluster you want to connect. Note: Enter only the cluster identifier and not the full endpoint of the Amazon Redshift cluster.

14. Enter the Database Name of the database to which you want to connect.

15. Enter a Database User to identify the user you want to use to connect to the database.

16. For UNLOAD IAM Role, enter the IAM role ARN of the role that the Amazon Redshift cluster should assume to move and write data to Amazon S3. For more information about this role, see Authorizing Amazon Redshift to access other AWS services on your behalf in the Amazon Redshift Management Guide.

17. Choose Connect.

18. (Optional) For Amazon S3 output location, specify the S3 URI to store the query results.

19. (Optional) For KMS key ID, specify the ARN of the AWS KMS key or alias. The following image shows you where you can find either key in the AWS Management Console.

    

To query and import data from Amazon Redshift

1. Select the connection that you want to query from Data Sources.

2. Select a Schema. To learn more about Amazon Redshift Schemas, see Schemas in the Amazon Redshift Database Developer Guide.

3. (Optional) Under Advanced configuration, specify the Sampling method that you'd like to use.

4. Enter your query in the query editor and choose Run to run the query. After a successful query, you can preview your result under the editor.

5. Select Import dataset to import the dataset that has been queried.

6. Enter a Dataset name. If you add a Dataset name that contains spaces, these spaces are replaced with underscores when your dataset is imported.

7. Choose Add.

To edit a dataset, do the following.

1. Navigate to your Data Wrangler flow.

2. Choose the + next to Source - Sampled.

3. Change the data that you're importing.

4. Choose Apply

To launch Studio Classic within a VPC, do the following.

1. Navigate to the SageMaker AI console at https://console.aws.amazon.com/sagemaker/.

2. Choose Launch SageMaker Studio Classic.

3. Choose Standard setup.

4. For Default execution role, choose the IAM role to set up Studio Classic.

5. Choose the VPC where you've launched the Amazon EMR clusters.

6. For Subnet, choose a private subnet.

7. For Security group(s), specify the security groups that you're using to control between your VPC.

8. Choose VPC Only.

9. (Optional) AWS uses a default encryption key. You can specify an AWS Key Management Service key to encrypt your data.

10. Choose Next.

11. Under Studio settings, choose the configurations that are best suited to you.

12. Choose Next to skip the SageMaker Canvas settings.

13. Choose Next to skip the RStudio settings.

To create a cluster, do the following.

1. Navigate to the AWS Management Console.

2. In the search bar, specify Amazon EMR.

3. Choose Create cluster.

4. For Cluster name, specify the name of your cluster.

5. For Release, select the release version of the cluster.

   Note

   Amazon EMR supports auto termination for the following releases:

   • For 6.x releases, releases 6.1.0 or later

   • For 5.x releases, releases 5.30.0 or later

   Auto termination stops idle clusters from running and prevents you from incurring costs.

6. (Optional) For Applications, choose Presto.

7. Choose the application that you're running on the cluster.

8. Under Networking, for Hardware configuration, specify the hardware configuration settings.

   Important

   For Networking, choose the VPC that is running Amazon SageMaker Studio Classic and choose a private subnet.

9. Under Security and access, specify the security settings.

10. Choose Create.

To import data from a cluster, do the following.

1. Open a Data Wrangler flow.

2. Choose Create Connection.

3. Choose Amazon EMR.

4. Do one of the following.

   • (Optional) For Secrets ARN, specify the Amazon Resource Number (ARN) of the database within the cluster. Secrets provide additional security. For more information about secrets, see What is AWS Secrets Manager? For information about creating a secret for your cluster, see Creating a AWS Secrets Manager secret for your cluster.

     Important

     You must specify a secret if you're using an IAM runtime role for authentication.

   • From the dropdown table, choose a cluster.

5. Choose Next.

6. For Select an endpoint for example-cluster-name cluster, choose a query engine.

7. (Optional) Select Save connection.

8. Choose Next, select login and choose one of the following:

   • No authentication

   • LDAP

   • IAM

9. For Login into example-cluster-name cluster, specify the Username and Password for the cluster.

10. Choose Connect.

11. In the query editor specify a SQL query.

12. Choose Run.

13. Choose Import.

To store your credentials as a secret, do the following.

1. Navigate to the AWS Management Console.

2. In the search bar, specify Secrets Manager.

3. Choose AWS Secrets Manager.

4. Choose Store a new secret.

5. For Secret type, choose Other type of secret.

6. Under Key/value pairs, select Plaintext.

7. For clusters running Hive, you can use the following template for IAM authentication.

   
   {"jdbcURL": ""
    "iam_auth": {"endpoint": "jdbc:hive2://", #required
                   "dns": "ip-xx-x-xxx-xxx.ec2.internal", #required 
                   "port": "10000", #required
                 "cluster_id": "j-xxxxxxxxx", #required
                 "iam_role": "arn:aws:iam::xxxxxxxx:role/xxxxxxxxxxxx", #optional
                 "truststore_path": "/etc/alternatives/jre/lib/security/cacerts", #optional
                 "truststore_password": "changeit" #optional
                 
                 }}                               
                               

   Note

   After you import your data, you apply transformations to them. You then export the data that you've transformed to a specific location. If you're using a Jupyter notebook to export your transformed data to Amazon S3, you must use the truststore path specified in the preceding example.

To store the JDBC URL as a secret, do the following.

1. Navigate to the AWS Management Console.

2. In the search bar, specify Secrets Manager.

3. Choose AWS Secrets Manager.

4. Choose Store a new secret.

5. For Secret type, choose Other type of secret.

6. For Key/value pairs, specify jdbcURL as the key and a valid JDBC URL as the value.

   The format of a valid JDBC URL depends on whether you use authentication and whether you use Hive or Presto as the query engine. The following list shows the valid JBDC URL formats for the different possible configurations.

   • Hive, no authentication – jdbc:hive2://emr-cluster-master-public-dns:10000/;

   • Hive, LDAP authentication – jdbc:hive2://emr-cluster-master-public-dns-name:10000/;AuthMech=3;UID=david;PWD=welcome123;

   • For Hive with SSL enabled, the JDBC URL format depends on whether you use a Java Keystore File for the TLS configuration. The Java Keystore File helps verify the identity of the master node of the Amazon EMR cluster. To use a Java Keystore File, generate it on an EMR cluster and upload it to Data Wrangler. To generate a file, use the following command on the Amazon EMR cluster, keytool -genkey -alias hive -keyalg RSA -keysize 1024 -keystore hive.jks. For information about running commands on an Amazon EMR cluster, see Securing access to EMR clusters using AWS Systems Manager. To upload a file, choose the upward arrow on the left-hand navigation of the Data Wrangler UI.

     The following are the valid JDBC URL formats for Hive with SSL enabled:

     • Without a Java Keystore File – jdbc:hive2://emr-cluster-master-public-dns:10000/;AuthMech=3;UID=user-name;PWD=password;SSL=1;AllowSelfSignedCerts=1;

     • With a Java Keystore File – jdbc:hive2://emr-cluster-master-public-dns:10000/;AuthMech=3;UID=user-name;PWD=password;SSL=1;SSLKeyStore=/home/sagemaker-user/data/Java-keystore-file-name;SSLKeyStorePwd=Java-keystore-file-passsword;

   • Presto, no authentication – jdbc:presto://emr-cluster-master-public-dns:8889/;

   • For Presto with LDAP authentication and SSL enabled, the JDBC URL format depends on whether you use a Java Keystore File for the TLS configuration. The Java Keystore File helps verify the identity of the master node of the Amazon EMR cluster. To use a Java Keystore File, generate it on an EMR cluster and upload it to Data Wrangler. To upload a file, choose the upward arrow on the left-hand navigation of the Data Wrangler UI. For information about creating a Java Keystore File for Presto, see Java Keystore File for TLS. For information about running commands on an Amazon EMR cluster, see Securing access to EMR clusters using AWS Systems Manager.

     • Without a Java Keystore File – jdbc:presto://emr-cluster-master-public-dns:8889/;SSL=1;AuthenticationType=LDAP Authentication;UID=user-name;PWD=password;AllowSelfSignedServerCert=1;AllowHostNameCNMismatch=1;

     • With a Java Keystore File – jdbc:presto://emr-cluster-master-public-dns:8889/;SSL=1;AuthenticationType=LDAP Authentication;SSLTrustStorePath=/home/sagemaker-user/data/Java-keystore-file-name;SSLTrustStorePwd=Java-keystore-file-passsword;UID=user-name;PWD=password;

To give permissions to Secrets Manager, do the following.

1. Sign in to the AWS Management Console and open the IAM console at https://console.aws.amazon.com/iam/.

2. Choose Roles.

3. In the search bar, specify the Amazon SageMaker AI execution role that Amazon SageMaker Studio Classic is using.

4. Choose the role.

5. Choose Add permissions.

6. Choose Create inline policy.

7. For Service, specify Secrets Manager and choose it.

8. For Actions, select the arrow icon next to Permissions management.

9. Choose PutResourcePolicy.

10. For Resources, choose Specific.

11. Choose the checkbox next to Any in this account.

12. Choose Review policy.

13. For Name, specify a name.

14. Choose Create policy.

To import data from Databricks, do the following.

1. Sign into Amazon SageMaker AI Console.

2. Choose Studio.

3. Choose Launch app.

4. From the dropdown list, select Studio.

5. From the Import data tab of your Data Wrangler flow, choose Databricks.

6. Specify the following fields:

   • Dataset name – A name that you want to use for the dataset in your Data Wrangler flow.

   • Driver – com.simba.spark.jdbc.Driver.

   • JDBC URL – The URL of the Databricks database. The URL formatting can vary between Databricks instances. For information about finding the URL and the specifying the parameters within it, see JDBC configuration and connection parameters. The following is an example of how a URL can be formatted: jdbc:spark://aws-sagemaker-datawrangler.cloud.databricks.com:443/default;transportMode=http;ssl=1;httpPath=sql/protocolv1/o/3122619508517275/0909-200301-cut318;AuthMech=3;UID=token;PWD=personal-access-token.

     Note

     You can specify a secret ARN that contains the JDBC URL instead of specifying the JDBC URL itself. The secret must contain a key-value pair with the following format: jdbcURL:JDBC-URL. For more information, see What is Secrets Manager?.

7. Specify a SQL SELECT statement.

   Note

   Data Wrangler doesn't support Common Table Expressions (CTE) or temporary tables within a query.

8. For Sampling, choose a sampling method.

9. Choose Run.

10. (Optional) For the PREVIEW, choose the gear to open the Partition settings.

    1. Specify the number of partitions. You can partition by column if you specify the number of partitions:

       • Enter number of partitions – Specify a value greater than 2.

       • (Optional) Partition by column – Specify the following fields. You can only partition by a column if you've specified a value for Enter number of partitions.

         • Select column – Select the column that you're using for the data partition. The data type of the column must be numeric or date.

         • Upper bound – From the values in the column that you've specified, the upper bound is the value that you're using in the partition. The value that you specify doesn't change the data that you're importing. It only affects the speed of the import. For the best performance, specify an upper bound that's close to the column's maximum.

         • Lower bound – From the values in the column that you've specified, the lower bound is the value that you're using in the partition. The value that you specify doesn't change the data that you're importing. It only affects the speed of the import. For the best performance, specify a lower bound that's close to the column's minimum.

11. Choose Import.

1. Navigate to the Salesforce login page.

2. For Quick find, specify My Domain.

3. Copy the value of Current My Domain URL to a text file.

4. Add https:// to the beginning of the URL.

To get the log in credentials from Salesforce and provide access to Data Wrangler, do the following.

1. Navigate to your Salesforce Domain URL and log into your account.

2. Choose the gear icon.

3. In the search bar that appears, specify App Manager.

4. Select New Connected App.

5. Specify the following fields:

   • Connected App Name – You can specify any name, but we recommend choosing a name that includes Data Wrangler. For example, you can specify Salesforce Data Cloud Data Wrangler Integration.

   • API name – Use the default value.

   • Contact Email – Specify your email address.

   • Under API heading (Enable OAuth Settings), select the checkbox to activate OAuth settings.

   • For Callback URL specify the Amazon SageMaker Studio Classic URL. To get the URL for Studio Classic, access it from the AWS Management Console and copy the URL.

6. Under Selected OAuth Scopes, move the following from the Available OAuth Scopes to Selected OAuth Scopes:

   • Manage user data via APIs (api)

   • Perform requests at any time (refresh_token, offline_access)

   • Perform ANSI SQL queries on Salesforce Data Cloud data (cdp_query_api)

   • Manage Salesforce Customer Data Platform profile data (cdp_profile_api)

7. Choose Save. After you save your changes, Salesforce opens a new page.

8. Choose Continue

9. Navigate to Consumer Key and Secret.

10. Choose Manage Consumer Details. Salesforce redirects you to a new page where you might have to pass two-factor authentication.

11. Important

    Copy the Consumer Key and Consumer Secret to a text editor. You need this information to connect the data cloud to Data Wrangler.

12. Navigate back to Manage Connected Apps.

13. Navigate to Connected App Name and the name of your application.

14. Choose Manage.

    1. Select Edit Policies.

    2. Change IP Relaxation to Relax IP restrictions.

    3. Choose Save.

To provide your users with permissions, do the following.

1. Navigate to the setup home page.

2. On the left-hand navigation, search for Users and choose the Users menu item.

3. Choose the hyperlink with your user name.

4. Navigate to Permission Set Assignments.

5. Choose Edit Assignments.

6. Add the following permissions:

   • Customer Data Platform Admin

   • Customer Data Platform Data Aware Specialist

7. Choose Save.

To get the authorization URL and token URL

1. Navigate to your Salesforce Domain URL.

2. Use one of the following methods to get the URLs. If you are on a Linux distribution with curl and jq installed, we recommend using the method that only works on Linux.

   • (Linux only) Specify the following command in your terminal.

     
     curl salesforce-domain-URL/.well-known/openid-configuration | \
     jq '. | { authorization_url: .authorization_endpoint, token_url: .token_endpoint }' | \
     jq '.  += { identity_provider: "SALESFORCE", client_id: "example-client-id", client_secret: "example-client-secret" }'                             
                                 

   • 1. Navigate to example-org-URL/.well-known/openid-configuration in your browser.

     2. Copy the authorization_endpoint and token_endpoint to a text editor.

     3. Create the following JSON object:

        {
          "identity_provider": "SALESFORCE",
          "authorization_url": "example-authorization-endpoint", 
          "token_url": "example-token-endpoint",
          "client_id": "example-consumer-key",
          "client_secret": "example-consumer-secret"
        }
                                            

To create a secret, do the following.

1. Navigate to the AWS Secrets Manager console.

2. Choose Store a secret.

3. Select Other type of secret.

4. Under Key/value pairs select Plaintext.

5. Replace the empty JSON with the following configuration settings.

   {
     "identity_provider": "SALESFORCE",
     "authorization_url": "example-authorization-endpoint", 
     "token_url": "example-token-endpoint",
     "client_id": "example-consumer-key",
     "client_secret": "example-consumer-secret"
   }                    
                       

6. Choose Next.

7. For Secret Name, specify the name of the secret.

8. Under Tags, choose Add.

   1. For the Key, specify sagemaker:partner. For Value, we recommend specifying a value that might be useful for your use case. However, you can specify anything.

   Important

   You must create the key. You can't import your data from Salesforce if you don't create it.

9. Choose Next.

10. Choose Store.

11. Choose the secret you've created.

12. Make a note of the following fields:

    • The Amazon Resource Number (ARN) of the secret

    • The name of the secret

To add read permissions for Data Wrangler, do the following.

1. Navigate to the Amazon SageMaker AI console.

2. Choose domains.

3. Choose the domain that you're using to access Data Wrangler.

4. Choose your User Profile.

5. Under Details, find the Execution role. Its ARN is in the following format: arn:aws:iam::111122223333:role/example-role. Make a note of the SageMaker AI execution role. Within the ARN, it's everything after role/.

6. Navigate to the IAM console.

7. In the Search IAM search bar, specify the name of the SageMaker AI execution role.

8. Choose the role.

9. Choose Add permissions.

10. Choose Create inline policy.

11. Choose the JSON tab.

12. Specify the following policy within the editor.

    
    {
     "Version": "2012-10-17",
     "Statement": [
         {
             "Effect": "Allow",
             "Action": [
                 "secretsmanager:GetSecretValue",
                 "secretsmanager:PutSecretValue"
             ],
             "Resource": "arn:aws:secretsmanager:*:*:secret:*",
             "Condition": {
                 "ForAnyValue:StringLike": {
                     "aws:ResourceTag/sagemaker:partner": "*"
                 }
             }
         },
         {
             "Effect": "Allow",
             "Action": [
                 "secretsmanager:UpdateSecret"
             ],
             "Resource": "arn:aws:secretsmanager:*:*:secret:AmazonSageMaker-*"
         }
     ]
    }                        
                        

13. Choose Review Policy.

14. For Name, specify a name.

15. Choose Create policy.

To create a lifecycle configuration and add it to the Studio Classic profile, do the following.

1. Navigate to the Amazon SageMaker AI console.

2. Choose domains.

3. Choose the domain that you're using to access Data Wrangler.

4. Choose your User Profile.

5. If you see the following applications, delete them:

   • KernelGateway

   • JupyterKernel

   Note

   Deleting the applications updates Studio Classic. It can take a while for the updates to happen.

6. While you're waiting for updates to happen, choose Lifecycle configurations.

7. Make sure the page you're on says Studio Classic Lifecycle configurations.

8. Choose Create configuration.

9. Make sure Jupyter server app has been selected.

10. Choose Next.

11. For Name, specify a name for the configuration.

12. For Scripts, specify the following script:

    
    #!/bin/bash
    set -eux
    
    cat > ~/.sfgenie_identity_provider_oauth_config <<EOL
    {
        "secret_arn": "secrets-arn-containing-salesforce-credentials"
    }
    EOL
    
    
                    

13. Choose Submit.

14. On the left hand navigation, choose domains.

15. Choose your domain.

16. Choose Environment.

17. Under Lifecycle configurations for personal Studio Classic apps, choose Attach.

18. Select Existing configuration.

19. Under Studio Classic Lifecycle configurations select the lifecycle configuration that you've created.

20. Choose Attach to domain.

21. Select the checkbox next to the lifecycle configuration that you've attached.

22. Select Set as default.

To open Studio Classic and check its version, see the following procedure.

1. Use the steps in Prerequisites to access Data Wrangler through Amazon SageMaker Studio Classic.

2. Next to the user you want to use to launch Studio Classic, select Launch app.

3. Choose Studio.

To create a dataset in Data Wrangler with data from the Salesforce Data Cloud

1. Sign into Amazon SageMaker AI Console.

2. Choose Studio.

3. Choose Launch app.

4. From the dropdown list, select Studio.

5. Choose the Home icon.

6. Choose Data.

7. Choose Data Wrangler.

8. Choose Import data.

9. Under Available, choose Salesforce Data Cloud.

10. For Connection name, specify a name for your connection to the Salesforce Data Cloud.

11. For Org URL, specify the organization URL in your Salesforce account. You can get the URL from your administrator.s

12. Choose Connect.

13. Specify your credentials to log into Salesforce.

1. Select the configuration that starts the process of registering Data Wrangler as an application.

2. Provide the users within the identity provider access to Data Wrangler.

3. Turn on OAuth client authentication by storing the client credentials as an AWS Secrets Manager secret.

4. Specify a redirect URL using the following format: https://domain-ID.studio.AWS Region.sagemaker.aws/jupyter/default/lab

   Important

   You're specifying the Amazon SageMaker AI domain ID and AWS Region that you're using to run Data Wrangler.

   Important

   You must register a URL for each Amazon SageMaker AI domain and AWS Region where you're running Data Wrangler. Users from a domain and AWS Region that don't have redirect URLs set up for them won't be able to authenticate with the identity provider to access the Snowflake connection.

5. Make sure that the authorization code and refresh token grant types are allowed for the Data Wrangler application.

To set up the server, do the following. You're working within Snowflake for all steps except the last one.

1. Start setting up the server or API.

2. Configure the authorization server to use the authorization code and refresh token grant types.

3. Specify the lifetime of the access token.

4. Set the refresh token idle timeout. The idle timeout is the time that the refresh token expires if it's not used.

   Note

   If you're scheduling jobs in Data Wrangler, we recommend making the idle timeout time greater than the frequency of the processing job. Otherwise, some processing jobs might fail because the refresh token expired before they could run. When the refresh token expires, the user must re-authenticate by accessing the connection that they've made to Snowflake through Data Wrangler.

5. Specify session:role-any as the new scope.

   Note

   For Azure AD, copy the unique identifier for the scope. Data Wrangler requires you to provide it with the identifier.

6. Important

   Within the External OAuth Security Integration for Snowflake, enable external_oauth_any_role_mode.

1. To allow your data scientist to access Snowflake from SageMaker Data Wrangler, provide them with one of the following:

   • For Basic Authentication, a Snowflake account name, user name, and password.

   • For OAuth, a user name and password in the identity provider.

   • For ARN, the Secrets Manager secret Amazon Resource Name (ARN).

   • A secret created with AWS Secrets Manager and the ARN of the secret. Use the following procedure below to create the secret for Snowflake if you choose this option.

     Important

     If your data scientists use the Snowflake Credentials (User name and Password) option to connect to Snowflake, you can use Secrets Manager to store the credentials in a secret. Secrets Manager rotates secrets as part of a best practice security plan. The secret created in Secrets Manager is only accessible with the Studio Classic role configured when you set up a Studio Classic user profile. This requires you to add this permission, secretsmanager:PutResourcePolicy, to the policy that is attached to your Studio Classic role.

     We strongly recommend that you scope the role policy to use different roles for different groups of Studio Classic users. You can add additional resource-based permissions for the Secrets Manager secrets. See Manage Secret Policy for condition keys you can use.

     For information about creating a secret, see Create a secret. You're charged for the secrets that you create.

2. (Optional) Provide the data scientist with the name of the storage integration that you created using the following procedure Create a Cloud Storage Integration in Snowflake. This is the name of the new integration and is called integration_name in the CREATE INTEGRATION SQL command you ran, which is shown in the following snippet:

   
     CREATE STORAGE INTEGRATION integration_name
     TYPE = EXTERNAL_STAGE
     STORAGE_PROVIDER = S3
     ENABLED = TRUE
     STORAGE_AWS_ROLE_ARN = 'iam_role'
     [ STORAGE_AWS_OBJECT_ACL = 'bucket-owner-full-control' ]
     STORAGE_ALLOWED_LOCATIONS = ('s3://bucket/path/', 's3://bucket/path/')
     [ STORAGE_BLOCKED_LOCATIONS = ('s3://bucket/path/', 's3://bucket/path/') ]
                                

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "glue:SearchTables",
            "Resource": [
                "arn:aws:glue:*:*:table/*/*",
                "arn:aws:glue:*:*:database/*",
                "arn:aws:glue:*:*:catalog"
            ]
        }
    ]
}   

To connect to a data source, do the following.

1. Sign into Amazon SageMaker AI Console.

2. Choose Studio.

3. Choose Launch app.

4. From the dropdown list, select Studio.

5. Choose the Home icon.

6. Choose Data.

7. Choose Data Wrangler.

8. Choose Import data.

9. Under Available, choose the data source.

10. For the Name field, specify the name of the connection.

11. (Optional) Choose Advanced configuration.

    1. Choose a Workgroup.

    2. If your workgroup hasn't enforced the Amazon S3 output location or if you don't use a workgroup, specify a value for Amazon S3 location of query results.

    3. (Optional) For Data retention period, select the checkbox to set a data retention period and specify the number of days to store the data before it's deleted.

    4. (Optional) By default, Data Wrangler saves the connection. You can choose to deselect the checkbox and not save the connection.

12. Choose Connect.

13. Specify a query.

    Note

    To help you specify a query, you can choose a table on the left-hand navigation panel. Data Wrangler shows the table name and a preview of the table. Choose the icon next to the table name to copy the name. You can use the table name in the query.

14. Choose Run.

15. Choose Import query.

16. For Dataset name, specify the name of the dataset.

17. Choose Add.