# Visual ETL for IAM-based domains
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IAM-based domains provide access to a new Amazon SageMaker Unified Studio interface where customers log in using federated roles and existing IAM permissions apply. The following sections describe how to use Visual ETL in IAM-based domains.

## Key features for IAM-based domains
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Visual ETL offers several capabilities to streamline your data workflows in IAM-based domains:
+ Drag-and-drop interface: Create Visual ETL flows by dragging and connecting components on a canvas.
+ Wide range of data connectors: Connect to various data sources and destinations, including databases, file systems, cloud storage, and APIs.
+ Extensive transformation library: Apply a variety of pre-built transformations to your data, such as filtering, aggregation, joining, and data type conversions.
+ Custom transformations: Create and save custom transformations using SQL or Python for reuse in multiple flows.
+ Data preview: Visualize your data at each step of the authoring process to ensure accuracy and data quality.
+ View scripts: View the code generated and choose to convert the flow to a notebook and continue authoring with code.
+ Code and compute configuration: Use a configuration panel to add code libraries and adjust the compute settings.

# Create a Visual ETL job in IAM-based domains
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To create a job using Visual ETL in Amazon SageMaker Unified Studio IAM-based domains:

1. Log in to Amazon SageMaker Unified Studio.

1. Navigate to the Visual ETL tool using the left menu, selecting "Visual ETL".

1. Choose "Create Visual job" to open the Visual ETL editor.

1. Give the job a name when you begin authoring the job and choose "save".

1. Open the "Add nodes" menu by choosing the plus icon and select a node, choosing your node from one of the three tabs: "Data sources", "Transforms", or "Data targets".

1. Drag a source component onto the canvas.

1. Configure the component by choosing the node and editing the configurations, to connect to your data source.

1. Add transformation components as needed, connecting them in the desired order.

1. Drag a data target onto the canvas and configure it to specify where the processed data should be stored.

1. Connect the components to create a complete job.

1. Choose the "Checklist" button to check for any configuration errors.

1. Choose "Save" when you are done correcting all errors.

1. Select "Run" to execute it immediately or choose the schedule icon to create a reoccurring run schedule.

# Authoring a Visual ETL job using generative AI in IAM-based domains
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To author a Visual ETL job using generative AI in Amazon SageMaker Unified Studio IAM-based domains:

1. Verify Amazon Q is enabled for your domain.

1. Open the Visual ETL editor.

1. In the "Add nodes" panel choose the Amazon Q icon.

1. (Optional) Choose "What can I ask?" and copy a prompt.

1. Enter the desired prompt in the chat box and choose 'Submit'.

1. Choose each node in the Visual ETL editor and configure its settings.

# Scheduling and running visual jobs in IAM-based domains
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There are two ways to schedule visual ETL jobs in Amazon SageMaker Unified Studio IAM-based domains:
+ You can schedule your visual jobs directly in the Visual ETL editor. This way you can schedule a single visual job quickly.
+ You can schedule your visual job using a DAG and the workflows interface. This way you can combine multiple elements in the same schedule.