AWS Step Functions adds support for AWS Glue DataBrew jobs to prepare data in analytics and machine learning workflows
You can now include AWS Glue DataBrew data preparation jobs in your workflows created using AWS Step Functions. This saves you time and allows you to orchestrate cleaning and data normalization steps into your analytics and machine learning workflows.
You can now include AWS Glue DataBrew data preparation jobs in your workflows created using AWS Step Functions. This saves you time and allows you to orchestrate cleaning and data normalization steps into your analytics and machine learning workflows.
AWS Step Functions allows you to build resilient workflows using AWS services such as AWS Glue, Amazon Athena, Amazon SageMaker, and AWS Lambda. AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning. In DataBrew, you can choose from over 250 pre-built transformations to create a data preparation recipe without the need to write any code. Now, with the integration of DataBrew jobs into Step Functions, you can orchestrate this recipe as part of existing analytics or machine learning workflows that include data pre-processing, cleaning and normalization tasks, quality checks, validation, and feature engineering for AWS data lakes, data warehouses, and databases.
To get started, read the Step Functions Developer Guide. AWS Step Functions support for AWS Glue DataBrew jobs is available in all regions where Step Functions and DataBrew are available. For a complete list of regions and service offerings, see AWS Regions.
To learn more about using AWS Glue DataBrew with AWS Step Functions, see: