AWS prescriptive-guidance documentation change
Summary
Fixed URL formatting by adding double slashes in AWS service documentation links
Security assessment
The changes only correct URL paths by adding an extra slash (//) after the domain in documentation links. There is no evidence of security vulnerability fixes, access control changes, or security feature additions. The modifications are purely cosmetic/formatting improvements for link validity.
Diff
diff --git a/prescriptive-guidance/latest/data-lake-for-growth-scale/data-consumer.md b/prescriptive-guidance/latest/data-lake-for-growth-scale/data-consumer.md index 2a1c35c6c..d6f200d45 100644 --- a//prescriptive-guidance/latest/data-lake-for-growth-scale/data-consumer.md +++ b//prescriptive-guidance/latest/data-lake-for-growth-scale/data-consumer.md @@ -15 +15 @@ There are two types of data consumer: _application_ and _data-serving_. The foll -**Data-serving type** | Data-serving data consumers are typically meant for individuals (for example, data analysts or data scientists) and applications (for example, a business intelligence application) that don't have their own AWS accounts. Multiple data-serving data consumers can exist in one organization’s data lake. For example, different lines of business might choose to set up their own data-serving data consumers to help users consume data from the data lake. These data consumers have their own IAM role principals configured in their AWS account (for example, IAM roles associated with [AWS IAM Identity Center](https://docs.aws.amazon.com/singlesignon/latest/userguide/what-is.html)) that are used by end users in the data consumer account to access shared data through AWS services (for example, [Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/what-is.html)). Typically, this type of data consumer has wide-ranging and continuously increasing data requirements. +**Data-serving type** | Data-serving data consumers are typically meant for individuals (for example, data analysts or data scientists) and applications (for example, a business intelligence application) that don't have their own AWS accounts. Multiple data-serving data consumers can exist in one organization’s data lake. For example, different lines of business might choose to set up their own data-serving data consumers to help users consume data from the data lake. These data consumers have their own IAM role principals configured in their AWS account (for example, IAM roles associated with [AWS IAM Identity Center](https://docs.aws.amazon.com//singlesignon/latest/userguide/what-is.html)) that are used by end users in the data consumer account to access shared data through AWS services (for example, [Amazon Athena](https://docs.aws.amazon.com//athena/latest/ug/what-is.html)). Typically, this type of data consumer has wide-ranging and continuously increasing data requirements. @@ -19 +19 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/what-is.html) is an interactive query service that helps directly analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. For more information about Athena and Lake Formation, see [How Athena accesses data registered with Lake Formation](https://docs.aws.amazon.com/athena/latest/ug/lf-athena-access.html) in the Amazon Athena documentation. + * [Amazon Athena](https://docs.aws.amazon.com//athena/latest/ug/what-is.html) is an interactive query service that helps directly analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. For more information about Athena and Lake Formation, see [How Athena accesses data registered with Lake Formation](https://docs.aws.amazon.com//athena/latest/ug/lf-athena-access.html) in the Amazon Athena documentation. @@ -21 +21 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [Amazon Redshift Spectrum](https://docs.aws.amazon.com/redshift/latest/dg/c-getting-started-using-spectrum.html) helps you to efficiently query and retrieve structured and semi-structured data from files in Amazon S3 without having to load the data into Amazon Redshift tables. For more information about Redshift Spectrum and Lake Formation, see [Using Redshift Spectrum with Lake Formation](https://docs.aws.amazon.com/redshift/latest/dg/spectrum-lake-formation.html) in the Amazon Redshift documentation. + * [Amazon Redshift Spectrum](https://docs.aws.amazon.com//redshift/latest/dg/c-getting-started-using-spectrum.html) helps you to efficiently query and retrieve structured and semi-structured data from files in Amazon S3 without having to load the data into Amazon Redshift tables. For more information about Redshift Spectrum and Lake Formation, see [Using Redshift Spectrum with Lake Formation](https://docs.aws.amazon.com//redshift/latest/dg/spectrum-lake-formation.html) in the Amazon Redshift documentation. @@ -23 +23 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between different data stores and data streams. An AWS Glue ETL job’s associated IAM role can access the data lake data managed by Lake Formation if it has the required access permissions. + * [AWS Glue](https://docs.aws.amazon.com//glue/latest/dg/what-is-glue.html) is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between different data stores and data streams. An AWS Glue ETL job’s associated IAM role can access the data lake data managed by Lake Formation if it has the required access permissions. @@ -25 +25 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [Amazon EMR](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-is-emr.html) helps run big data frameworks (for example, [Apache Hadoop](https://aws.amazon.com/elasticmapreduce/details/hadoop) and [Apache Spark](https://aws.amazon.com/elasticmapreduce/details/spark)) to process and analyze large amounts of data. For more information about Amazon EMR and Lake Formation, see [Integrate Amazon EMR with Lake Formation](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-lake-formation.html) in the Amazon EMR documentation. + * [Amazon EMR](https://docs.aws.amazon.com//emr/latest/ManagementGuide/emr-what-is-emr.html) helps run big data frameworks (for example, [Apache Hadoop](https://aws.amazon.com//elasticmapreduce/details/hadoop) and [Apache Spark](https://aws.amazon.com//elasticmapreduce/details/spark)) to process and analyze large amounts of data. For more information about Amazon EMR and Lake Formation, see [Integrate Amazon EMR with Lake Formation](https://docs.aws.amazon.com//emr/latest/ManagementGuide/emr-lake-formation.html) in the Amazon EMR documentation. @@ -27 +27 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [Amazon Quick Suite](https://docs.aws.amazon.com/quicksight/latest/user/welcome.html) is a scalable, serverless, embeddable, and machine learning (ML)-powered business intelligence service that you can use to analyze and visualize data from your data lake. For more information about Quick Suite and Lake Formation, see [Authorizing connections through Lake Formation](https://docs.aws.amazon.com/quicksight/latest/user/lake-formation.html) in the Quick Suite documentation. + * [Amazon Quick Suite](https://docs.aws.amazon.com//quicksight/latest/user/welcome.html) is a scalable, serverless, embeddable, and machine learning (ML)-powered business intelligence service that you can use to analyze and visualize data from your data lake. For more information about Quick Suite and Lake Formation, see [Authorizing connections through Lake Formation](https://docs.aws.amazon.com//quicksight/latest/user/lake-formation.html) in the Quick Suite documentation. @@ -29 +29 @@ AWS Lake Formation is the most important AWS service used by a data consumer for - * [Amazon SageMaker AI Data Wrangler (Data Wrangler)](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html) reduces the time it takes to aggregate and prepare data for ML. For more information about Data Wrangler and Lake Formation, see [Prepare ML Data with Amazon SageMaker AI Data Wrangler](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html) in the Amazon SageMaker AI documentation. + * [Amazon SageMaker AI Data Wrangler (Data Wrangler)](https://docs.aws.amazon.com//sagemaker/latest/dg/data-wrangler.html) reduces the time it takes to aggregate and prepare data for ML. For more information about Data Wrangler and Lake Formation, see [Prepare ML Data with Amazon SageMaker AI Data Wrangler](https://docs.aws.amazon.com//sagemaker/latest/dg/data-wrangler.html) in the Amazon SageMaker AI documentation.