AWS emr documentation change
Summary
Minor wording updates and grammatical improvements in Lake Formation integration documentation. Changes include replacing 'see' with 'refer to', adjusting phrasing for conciseness ('you can leverage' → 'leverage'), and clarifying resource allocation recommendations.
Security assessment
The changes are editorial improvements rather than security-related updates. While Lake Formation itself is a security feature for access control, these documentation tweaks do not introduce new security capabilities, address vulnerabilities, or modify security guidance. The mention of inter-worker encryption being enabled by default was already present in the original text.
Diff
diff --git a/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable.md b/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable.md index b5788089f..d3a2462a3 100644 --- a//emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable.md +++ b//emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable.md @@ -11 +11 @@ OverviewHow it worksEnable Lake FormationEnable runtime permissionsSet up runtim -With Amazon EMR releases 7.2.0 and higher, you can leverage AWS Lake Formation to apply fine-grained access controls on Data Catalog tables that are backed by S3. This capability lets you configure table, row, column, and cell level access controls for read queries within your Amazon EMR Serverless Spark jobs. To configure fine-grained access control for Apache Spark batch jobs and interactive sessions, use EMR Studio. See the following sections to learn more about Lake Formation and how to use it with EMR Serverless. +With Amazon EMR releases 7.2.0 and higher, leverage AWS Lake Formation to apply fine-grained access controls on Data Catalog tables that are backed by S3. This capability lets you configure table, row, column, and cell level access controls for read queries within your Amazon EMR Serverless Spark jobs. To configure fine-grained access control for Apache Spark batch jobs and interactive sessions, use EMR Studio. See the following sections to learn more about Lake Formation and how to use it with EMR Serverless. @@ -13 +13 @@ With Amazon EMR releases 7.2.0 and higher, you can leverage AWS Lake Formation t -Using Amazon EMR Serverless with AWS Lake Formation incurs additional charges. For more information, see [Amazon EMR pricing](https://aws.amazon.com/emr/pricing/). +Using Amazon EMR Serverless with AWS Lake Formation incurs additional charges. For more information, refer to [Amazon EMR pricing](https://aws.amazon.com/emr/pricing/). @@ -17 +17 @@ Using Amazon EMR Serverless with AWS Lake Formation incurs additional charges. F -Using EMR Serverless with Lake Formation lets you enforce a layer of permissions on each Spark job to apply Lake Formation permissions control when EMR Serverless executes jobs. EMR Serverless uses [ Spark resource profiles](https://spark.apache.org/docs/latest/api/java/org/apache/spark/resource/ResourceProfile.html) to create two profiles to effectively execute jobs. The user profile executes user-supplied code, while the system profile enforces Lake Formation policies. For more information, see [What is AWS Lake Formation](https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html) and [Considerations and limitations](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable-considerations.html). +Using EMR Serverless with Lake Formation lets you enforce a layer of permissions on each Spark job to apply Lake Formation permissions control when EMR Serverless executes jobs. EMR Serverless uses [ Spark resource profiles](https://spark.apache.org/docs/latest/api/java/org/apache/spark/resource/ResourceProfile.html) to create two profiles to effectively execute jobs. The user profile executes user-supplied code, while the system profile enforces Lake Formation policies. For more information, refer to [What is AWS Lake Formation](https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html) and [Considerations and limitations](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable-considerations.html). @@ -19 +19 @@ Using EMR Serverless with Lake Formation lets you enforce a layer of permissions -When you use pre-initialized capacity with Lake Formation, we recommend that you have a minimum of two Spark drivers. Each Lake Formation-enabled job utilizes two Spark drivers, one for the user profile and one for the system profile. For the best performance, you should use double the number of drivers for Lake Formation-enabled jobs compared to if you don't use Lake Formation. +When you use pre-initialized capacity with Lake Formation, we suggest that you have a minimum of two Spark drivers. Each Lake Formation-enabled job utilizes two Spark drivers, one for the user profile and one for the system profile. For the best performance, use double the number of drivers for Lake Formation-enabled jobs compared to if you don't use Lake Formation. @@ -21 +21 @@ When you use pre-initialized capacity with Lake Formation, we recommend that you -When you run Spark jobs on EMR Serverless, you must also consider the impact of dynamic allocation on resource management and cluster performance. The configuration `spark.dynamicAllocation.maxExecutors` of the maximum number of executors per resource profile applies to both user and system executors. If you configure that number to be equal to the maximum allowed number of executors, your job run might get stuck because of one type of executor that uses all available resources, which prevents the other executor when you run jobs jobs. +When you run Spark jobs on EMR Serverless, also consider the impact of dynamic allocation on resource management and cluster performance. The configuration `spark.dynamicAllocation.maxExecutors` of the maximum number of executors per resource profile applies to user and system executors. If you configure that number to be equal to the maximum allowed number of executors, your job run might get stuck because of one type of executor that uses all available resources, which prevents the other executor when you run jobs jobs. @@ -23 +23 @@ When you run Spark jobs on EMR Serverless, you must also consider the impact of -So you don't run out of resources, EMR Serverless sets the default maximum number of executors per resource profile to 90% of the `spark.dynamicAllocation.maxExecutors` value. You can override this configuration when you specify `spark.dynamicAllocation.maxExecutorsRatio` with a value between 0 and 1. Additionally, you can also configure the following properties to optimize resource allocation and overall performance: +So you don't run out of resources, EMR Serverless sets the default maximum number of executors per resource profile to 90% of the `spark.dynamicAllocation.maxExecutors` value. You can override this configuration when you specify `spark.dynamicAllocation.maxExecutorsRatio` with a value between 0 and 1. Additionally, also configure the following properties to optimize resource allocation and overall performance: @@ -36 +36 @@ The following is a high-level overview of how EMR Serverless gets access to data - + @@ -53 +53 @@ The following is a high-level overview of how EMR Serverless gets access to data -To enable Lake Formation, you must set `spark.emr-serverless.lakeformation.enabled` to `true` under `spark-defaults` classification for the runtime-configuration parameter when [ creating an EMR Serverless application](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/getting-started.html#gs-application-console). +To enable Lake Formation, set `spark.emr-serverless.lakeformation.enabled` to `true` under `spark-defaults` classification for the runtime-configuration parameter when [ creating an EMR Serverless application](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/getting-started.html#gs-application-console). @@ -68 +68 @@ You can also enable Lake Formation when you create a new application in EMR Stud -[Inter-worker encryption](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/interworker-encryption.html) is enabled by default when you use Lake Formation with EMR Serverless, so you don't have to explicitly enable inter-worker encryption again. +[Inter-worker encryption](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/interworker-encryption.html) is enabled by default when you use Lake Formation with EMR Serverless, so you do not need to explicitly enable inter-worker encryption again. @@ -143 +143 @@ JSON -First, register the location of your Hive table with Lake Formation. Then create permissions for your job runtime role on your desired table. For more details about Lake Formation, see [ What is AWS Lake Formation?](https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html) in the _AWS Lake Formation Developer Guide_. +First, register the location of your Hive table with Lake Formation. Then create permissions for your job runtime role on your desired table. For more details about Lake Formation, refer to [ What is AWS Lake Formation?](https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html) in the _AWS Lake Formation Developer Guide_. @@ -145 +145 @@ First, register the location of your Hive table with Lake Formation. Then create -After you set up the Lake Formation permissions, you can submit Spark jobs on Amazon EMR Serverless. For more information about Spark jobs, see [Spark examples](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/jobs-spark.html#spark-examples). +After you set up the Lake Formation permissions, submit Spark jobs on Amazon EMR Serverless. For more information about Spark jobs, refer to [Spark examples](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/jobs-spark.html#spark-examples). @@ -184 +184 @@ Metadata tables | Supported, but certain tables are hidden. Refer to [considerat -Stored procedures | Supported with the exceptions of `register_table` and `migrate`. See [considerations and limitations](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable-considerations.html) for more information. +Stored procedures | Supported with the exceptions of `register_table` and `migrate`. Refer to [considerations and limitations](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless-lf-enable-considerations.html) for more information.