AWS glue documentation change
Summary
Restructured documentation about AWS Glue Studio ETL jobs and added integration with Amazon SageMaker workflows
Security assessment
The changes focus on improving documentation structure and adding workflow integration details. No security features, vulnerabilities, or configuration guidance were introduced or modified.
Diff
diff --git a/glue/latest/dg/author-job-glue.md b/glue/latest/dg/author-job-glue.md index 36f8e7e03..2c0b75f2a 100644 --- a//glue/latest/dg/author-job-glue.md +++ b//glue/latest/dg/author-job-glue.md @@ -5 +5 @@ -Signing in to the consoleNext steps for creating a job in AWS Glue Studio +Build visual ETL jobs with AWS Glue StudioBuild visual ETL flows with Amazon SageMaker @@ -7 +7 @@ Signing in to the consoleNext steps for creating a job in AWS Glue Studio -# Building visual ETL jobs with AWS Glue Studio +# Building visual ETL jobs @@ -9 +9,5 @@ Signing in to the consoleNext steps for creating a job in AWS Glue Studio -An AWS Glue job encapsulates a script that connects to your source data, processes it, and then writes it out to your data target. Typically, a job runs extract, transform, and load (ETL) scripts. Jobs can run scripts designed for Apache Spark and Ray runtime environments. Jobs can also run general-purpose Python scripts (Python shell jobs.) AWS Glue _triggers_ can start jobs based on a schedule or event, or on demand. You can monitor job runs to understand runtime metrics such as completion status, duration, and start time. +## Build visual ETL jobs with AWS Glue Studio + +AWS Glue Studio provides a visual interface for creating, running, and monitoring Extract/Transform/Load (ETL) jobs in AWS Glue. A job in AWS Glue consists of the business logic that performs extract, transform, and load (ETL) work. With AWS Glue Studio, you can visually compose data transformation workflows and seamlessly run them on AWS Glue's Apache Spark-based serverless ETL engine. You can create jobs that move and transform data between various data stores and streams using a drag-and-drop interface without having to learn Spark or write code. + +An AWS Glue job encapsulates a script that connects to your source data, processes it, and then writes it out to your data target. Typically, a job runs extract, transform, and load (ETL) scripts. Jobs can run scripts designed for Apache Spark and Ray runtime environments. Jobs can also run general-purpose Python scripts (Python shell jobs.) AWS Glue triggers can start jobs based on a schedule or event, or on demand. You can monitor job runs to understand runtime metrics such as completion status, duration, and start time. @@ -15,3 +19 @@ AWS Glue can write output files in several data formats. Each job type may suppo -## Signing in to the AWS Glue console - -A job in AWS Glue consists of the business logic that performs extract, transform, and load (ETL) work. You can create jobs in the **ETL** section of the AWS Glue console. +### Managing AWS Glue Jobs in the AWS Console @@ -21 +23 @@ To view existing jobs, sign in to the AWS Management Console and open the AWS Gl -While creating a new job, or after you have saved your job, you can use can AWS Glue Studio to modify your ETL jobs. You can do this by editing the nodes in the visual editor or by editing the job script in developer mode. You can also add and remove nodes in the visual editor to create more complicated ETL jobs. +You can create jobs in the **ETL** section of the AWS Glue console. While creating a new job, or after you have saved your job, you can use can AWS Glue Studio to modify your ETL jobs. You can do this by editing the nodes in the visual editor or by editing the job script in developer mode. You can also add and remove nodes in the visual editor to create more complicated ETL jobs. @@ -23 +25 @@ While creating a new job, or after you have saved your job, you can use can AWS -## Next steps for creating a job in AWS Glue Studio +### Next steps for creating a job in AWS Glue Studio @@ -45,0 +48,4 @@ The next steps for creating and managing your jobs are: +## Build visual ETL flows with Amazon SageMaker + +With an Amazon SageMaker Unified Studio workflow, you can set up and run a series of tasks in Amazon SageMaker Unified Studio. Amazon SageMaker Unified Studio workflows use Apache Airflow to model data processing procedures and orchestrate your Amazon SageMaker Unified Studio code artifacts. For more information, see [ Using workflows in Amazon SageMaker Unified Studio ](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/workflow-orchestration.html). +