Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Working with Amazon Glue Data Catalog views (preview)

You can create and manage single common views in the Amazon Glue Data Catalog. Single common views are useful because they support multiple SQL query engines, so you can access the same view across different Amazon Web Services, such as Amazon EMR Amazon Athena, and Amazon Redshift.

By creating a view in the Data Catalog, you can use resource grants and tag-based access controls in Amazon Lake Formation to grant access to a Data Catalog view. Using this method of access control, you don't have to configure additional access to the tables you referenced when creating the view. This method of granting permissions is called definer semantics, and these views are called definer views. For more information about access control in Lake Formation, see Granting and revoking permissions on Data Catalog resources. in the Amazon Lake Formation Developer Guide.

Data Catalog views are useful for the following use cases:

Creating a Data Catalog view

To create a Data Catalog view, you must use an IAM role that has the full SELECT permission with Grantable options on all of the tables you want to reference when creating the view. This role is called the definer role. For a full list of permissions and prerequisites required to create a Data Catalog view, see Working with views in the Amazon Lake Formation Developer Guide. You must use the Amazon CLI to configure your IAM role. See Use an IAM role in the Amazon CLI for more information.

Follow these steps to create a Data Catalog view.

Enabling access to a Data Catalog view

To access the Data Catalog view from Apache Spark on Amazon EMR, you must first enable support for Lake Formation and use the script below to enable support for views with Spark on Amazon EMR. For more information about enabling support, see Enable Lake Formation with Amazon EMR and Use custom bootstrap actions.

# Download the script and upload it to Amazon S3
wget https://emr-data-access-control-us-east-1.s3.amazonaws.com/beta/glue-views/ba/enable-mdv.sh /Users/$USER/enable-mdv.sh
aws s3 cp /Users/$USER/enable-views.sh s3://<bucket>/<prefix>/enable-views.sh

# EMR Security Configuration
cat <<EOT > /Users/$USER/lakeformation-protection.json 
{
    "AuthorizationConfiguration":{
        "IAMConfiguration":{
            "EnableApplicationScopedIAMRole":true
        },
        "LakeFormationConfiguration":{
            "AuthorizedSessionTagValue":"Amazon EMR"
        }
    },
     "EncryptionConfiguration": {
            "EnableInTransitEncryption": true,
            "InTransitEncryptionConfiguration": {
            "TLSCertificateConfiguration": {
                 "CertificateProviderType": "PEM",
                 "S3Object": "s3://<BUCKET>/<PREFIX>/certificates.zip"
            }
        }
    }
}
EOT

SECURITY_CONFIG="RuntimeRolesWithAWSLakeFormation"

aws emr create-security-configuration \
--name $SECURITY_CONFIG \
--security-configuration file:///Users/$USER/lakeformation-protection.json

# EMR Cluster version
RELEASE_LABEL="emr-6.15.0"

Then use the following Amazon CLI command that uses the bootstrap action to create an EMR cluster that supports Data Catalog views.

aws emr create-cluster \
...
--release-label $RELEASE_LABEL \
--security-configuration $SECURITY_CONFIG \
--bootstrap-actions \
 Name='Enable Views',Path="s3://<bucket>/<prefix>/enable-views.sh"

Querying a Data Catalog view

After creating a Data Catalog view, you can now use an IAM role to query the view. The IAM role must have the SELECT permission on the Data Catalog view. You don't need to grant access to the underlying tables referred in the view. You must use this IAM role as a runtime role. You can access the view from an EMR cluster using a runtime role from Amazon EMR steps, EMR Studio, and SageMaker Studio. For more information about runtime roles, see Runtime roles for Amazon EMR steps.

Once you have everything set up, you can query your view. For example, after attaching the EMR cluster to your Workspace in EMR Studio, you can run the following query to access a view.

SELECT * from <database>.<glue-data-catalog-view> LIMIT 10

Limitations

Consider the following limitations when you use Data Catalog views.