AWS prescriptive-guidance medium security documentation change
Summary
Updated terminology (S3 to Amazon S3), clarified PII references, fixed formatting, and adjusted links to sensitive data documentation
Security assessment
The change explicitly references handling of PII data and links to a sensitive data section, emphasizing security practices for sensitive information. The recommendation to mask data before moving it to raw storage demonstrates security-conscious data handling.
Diff
diff --git a/prescriptive-guidance/latest/defining-bucket-names-data-lakes/data-layer-definitions.md b/prescriptive-guidance/latest/defining-bucket-names-data-lakes/data-layer-definitions.md index 9821f64b5..9da38bb2c 100644 --- a//prescriptive-guidance/latest/defining-bucket-names-data-lakes/data-layer-definitions.md +++ b//prescriptive-guidance/latest/defining-bucket-names-data-lakes/data-layer-definitions.md @@ -3 +3 @@ -[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Defining S3 bucket and path names for data lake layers on the AWS Cloud](welcome.html) +[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Defining Amazon S3 bucket and path names for data lake layers](welcome.html) @@ -7 +7 @@ -If you work with non-sensitive data, such as non-personally identifiable information (PII) data, we recommend that you use at least three different data layers in a data lake on the AWS Cloud. +If you work with non-sensitive data, such as data that doesn't contain personally identifiable information (PII), we recommend that you use at least three different data layers in a data lake on the AWS Cloud. @@ -9 +9 @@ If you work with non-sensitive data, such as non-personally identifiable informa -However, you might require additional layers depending on the data’s complexity and use cases. For example, if you work with sensitive data (for example, PII data), we recommend that you use an additional Amazon Simple Storage Service (Amazon S3) bucket as a landing zone and then mask the data before it is moved into the raw data layer. For more information about this, see the [Handling sensitive data](./handling-sensitive-data.html) section of this guide. +However, you might require additional layers depending on the data's complexity and use cases. For example, if you work with sensitive data, such as PII data, we recommend that you use an additional Amazon Simple Storage Service (Amazon S3) bucket as a landing zone. You then mask the data before it is moved into the raw data layer. For more information about this, see the [Handling sensitive data](./sensitive-data.html) section of this guide. @@ -11 +11 @@ However, you might require additional layers depending on the data’s complexit -Each data layer must have an individual S3 bucket; the following table describes our recommended data layers: +Each data layer must have an individual Amazon S3 bucket. The following table describes the recommended data layers. @@ -13 +13 @@ Each data layer must have an individual S3 bucket; the following table describes -**Data layer name** | **Description** | **Sample lifecycle policy strategy** +Data layer name | Description | Sample lifecycle policy strategy @@ -15,3 +15,3 @@ Each data layer must have an individual S3 bucket; the following table describes -_Raw_ | Contains the raw, unprocessed data and is the layer in which data is ingested into the data lake. If possible, you should keep the original file format and turn on versioning in the S3 bucket. | After one year, move files into the [Amazon S3 infrequent access (IA) storage class](https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html). After two years in Amazon S3 IA, archive them to [Amazon S3 Glacier](https://docs.aws.amazon.com/amazonglacier/latest/dev/introduction.html). -_Stage_ | Contains intermediate, processed data that is optimized for consumption (for example CSV to Apache Parquet converted raw files or data transformations). An AWS Glue job reads the files from the raw layer and validates the data. The AWS Glue job then stores the data in an Apache Parquet-formatted file and the metadata is stored in a table in the AWS Glue Data Catalog. | Data can be deleted after a defined time period or according to your organization's requirements. Some data derivatives (for example, an Apache Avro transform of an original JSON format) can be removed from the data lake after a shorter amount of time (for example, after 90 days). -_Analytics_ | Contains the aggregated data for your specific use cases in a consumption-ready format (for example, Apache Parquet). | Data can be moved to Amazon S3 IA and then deleted after a defined time period or according to your organization's requirements. +_Raw_ | Contains the raw, unprocessed data. Data is ingested into the data lake in this layer.If possible, you should keep the original file format and turn on versioning in the Amazon S3 bucket. | After one year, move files into the [Amazon S3 infrequent access (IA) storage class](https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html#sc-infreq-data-access). After two years in Amazon S3 IA, archive them to [Amazon S3 Glacier storage classes](https://docs.aws.amazon.com/AmazonS3/latest/userguide/glacier-storage-classes.html). +_Stage_ | Contains intermediate, processed data that is optimized for consumption (for example CSV to Apache Parquet converted raw files or data transformations).An AWS Glue job reads the files from the raw layer and validates the data. The AWS Glue job then stores the data in an Apache Parquet-formatted file, and the metadata is stored in a table in the AWS Glue Data Catalog. | Data can be deleted after a defined time period or according to your organization's requirements.Some data derivatives, such as an Apache Avro transform of an original JSON format, can be removed from the data lake after a shorter amount of time, such as after 90 days. +_Analytics_ | Contains the aggregated data for your specific use cases in a consumption-ready format, such as Apache Parquet. | Data can be moved to Amazon S3 IA and then deleted after a defined time period or according to your organization's requirements. @@ -31 +31 @@ Introduction -Naming S3 buckets in your data layers +Naming Amazon S3 buckets