AWS emr documentation change
Summary
Minor grammatical and phrasing updates throughout the document, including replacing 'shows' with 'demonstrates', adjusting verb forms, and updating cross-reference wording (e.g., 'see' to 'refer to'). No technical content changes.
Security assessment
The changes are purely editorial improvements to enhance readability. There are no additions or modifications to security-related content beyond rephrasing existing explanations about Lake Formation access controls and definer semantics. No vulnerabilities or new security features are addressed.
Diff
diff --git a/emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-glue-data-catalog-views.md b/emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-glue-data-catalog-views.md index b068177a3..1e366a1ef 100644 --- a//emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-glue-data-catalog-views.md +++ b//emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-glue-data-catalog-views.md @@ -11 +11 @@ You can create and manage views in the AWS Glue Data Catalog for use with EMR Se -By creating a view in the Data Catalog, you can use resource grants and tag-based access controls in AWS Lake Formation to grant access to it. 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](https://docs.aws.amazon.com/lake-formation/latest/dg/granting-catalog-permissions.html) in the AWS Lake Formation Developer Guide. +By creating a view in the Data Catalog, use resource grants and tag-based access controls in AWS Lake Formation to grant access to it. Using this method of access control, you do not need 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](https://docs.aws.amazon.com/lake-formation/latest/dg/granting-catalog-permissions.html) in the AWS Lake Formation Developer Guide. @@ -21 +21 @@ Data Catalog views are useful for the following use cases: - * **Simple sharing data** – Share data with other AWS accounts without moving data. For more information, see [Cross-account data sharing in Lake Formation](https://docs.aws.amazon.com/lake-formation/latest/dg/cross-account-permissions.html). + * **Simple sharing data** – Share data with other AWS accounts without moving data. For more information, refer to [Cross-account data sharing in Lake Formation](https://docs.aws.amazon.com/lake-formation/latest/dg/cross-account-permissions.html). @@ -33 +33 @@ Using SQL -The following shows the syntax for creating a Data Catalog view. Note the `MULTI DIALECT` view type. This distinguishes the Data Catalog view from other views. The `SECURITY` predicate is specified as `DEFINER`. This indicates a Data Catalog view with `DEFINER` semantics. +The following demonstrates the syntax for creating a Data Catalog view. Note the `MULTI DIALECT` view type. This distinguishes the Data Catalog view from other views. The `SECURITY` predicate is specified as `DEFINER`. This indicates a Data Catalog view with `DEFINER` semantics. @@ -129 +129 @@ Available syntax: -You can use the `FORCE ADD DIALECT` option to force update the schema and sub objects as per the new engine dialect. Note that doing this can result in query errors if you don't also use `FORCE` to update other engine dialects. The following shows a sample: +You can use the `FORCE ADD DIALECT` option to force update the schema and sub objects as per the new engine dialect. Note that doing this can result in query errors if you don't also use `FORCE` to update other engine dialects. The following demonstrates a sample: @@ -137 +137 @@ You can use the `FORCE ADD DIALECT` option to force update the schema and sub ob -The following shows how to alter a view in order to update the dialect: +The following demonstrates how to alter a view to update the dialect: @@ -146 +146 @@ Available syntax for describing a view: - * `SHOW COLUMNS {FROM|IN} view_name [{FROM|IN} database_name]` – If the user has the required AWS Glue and Lake Formation permissions to describe the view, they can list the columns. The following shows a couple sample commands for showing columns: + * `SHOW COLUMNS {FROM|IN} view_name [{FROM|IN} database_name]` – If the user has the required AWS Glue and Lake Formation permissions to describe the view, they can list the columns. The following demonstrates a couple sample commands for showing columns: @@ -184 +184 @@ List all views in the catalog such asregular views, multi-dialect views (MDV), a -The following shows a sample command to show views: +The following demonstrates a sample command to show views: @@ -191 +191 @@ The following shows a sample command to show views: -For more information about creating and configuring data-catalog views, see [Building AWS Glue Data Catalog views](https://docs.aws.amazon.com/lake-formation/latest/dg/working-with-views.html) in the AWS Lake Formation Developer Guide. +For more information about creating and configuring data-catalog views, refer to [Building AWS Glue Data Catalog views](https://docs.aws.amazon.com/lake-formation/latest/dg/working-with-views.html) in the AWS Lake Formation Developer Guide. @@ -197 +197 @@ After creating a Data Catalog view, you can query it using an Amazon EMR Serverl -Once you have everything set up, you can query your view. For example, after creating an EMR Serverless application in EMR Studio, you can run the following query to access a view. +Once you have everything set up, you can query your view. For example, after creating an EMR Serverless application in EMR Studio, run the following query to access a view. @@ -219 +219 @@ When you create Data Catalog views, the following apply: - * The table's Amazon S3 location must be registered as a Lake Formation data lake location. If the table isn't registered, the error _Multi Dialect views may only reference Lake Formation managed tables_ occurs. For information about how to register Amazon S3 locations in Lake Formation, see [Registering an Amazon S3 location](https://docs.aws.amazon.com/lake-formation/latest/dg/register-location.html) in the AWS Lake Formation Developer Guide. + * The table's Amazon S3 location must be registered as a Lake Formation data lake location. If the table isn't registered, the error _Multi Dialect views may only reference Lake Formation managed tables_ occurs. For information about how to register Amazon S3 locations in Lake Formation, refer to [Registering an Amazon S3 location](https://docs.aws.amazon.com/lake-formation/latest/dg/register-location.html) in the AWS Lake Formation Developer Guide. @@ -233 +233 @@ When you create Data Catalog views, the following apply: - * An AWS Glue Data Catalog view schema is always stored using lowercase. For example, if you use a DDL statement to create a Glue Data Catalog view with a column named `Castle`, the column created in the Glue Data Catalog will be made lowercase, to `castle`. If you then specify the column name in a DML query as `Castle` or `CASTLE`, EMR Spark will make the name lowercase for you in order to run the query. But the column heading displays using the casing that you specified in the query. + * An AWS Glue Data Catalog view schema is always stored using lowercase. For example, if you use a DDL statement to create a Glue Data Catalog view with a column named `Castle`, the column created in the Glue Data Catalog will be made lowercase, to `castle`. If you then specify the column name in a DML query as `Castle` or `CASTLE`, EMR Spark will make the name lowercase for you to run the query. But the column heading displays using the casing that you specified in the query. @@ -235 +235 @@ When you create Data Catalog views, the following apply: -If you want a query to fail in a case where a column name specified in the DML query does not match the column name in the Glue Data Catalog, you can set `spark.sql.caseSensitive=true`. +If you want a query to fail in a case where a column name specified in the DML query does not match the column name in the Glue Data Catalog, set `spark.sql.caseSensitive=true`.