Start AWS Glue DataBrew jobs with Step Functions - AWS Step Functions

Start AWS Glue DataBrew jobs with Step Functions

Learn how you can use the DataBrew integration to add data cleaning and data normalization steps into your analytics and machine learning workflows with Step Functions.

To learn about integrating with AWS services in Step Functions, see Integrating services and Passing parameters to a service API in Step Functions.

The following includes a Task state that starts a request-response DataBrew job.

"DataBrew StartJobRun": { "Type": "Task", "Resource": "arn:aws:states:::databrew:startJobRun", "Parameters": { "Name": "sample-proj-job-1" }, "Next": "NEXT_STATE" },

The following includes a Task state that starts a sync DataBrew job.

"DataBrew StartJobRun": { "Type": "Task", "Resource": "arn:aws:states:::databrew:startJobRun.sync", "Parameters": { "Name": "sample-proj-job-1" }, "Next": "NEXT_STATE" },
Parameters in Step Functions are expressed in PascalCase

Even if the native service API is in camelCase, for example the API action startSyncExecution, you specify parameters in PascalCase, such as: StateMachineArn.

Supported DataBrew APIs

IAM policies for calling DataBrew

The following example templates show how AWS Step Functions generates IAM policies based on the resources in your state machine definition. For more information, see How Step Functions generates IAM policies for integrated services and Discover service integration patterns in Step Functions.

Run a Job (.sync)
{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "databrew:startJobRun", "databrew:listJobRuns", "databrew:stopJobRun" ], "Resource": [ "arn:aws:databrew:{{region}}:{{accountId}}:job/*" ] } ] }
Request Response
{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "databrew:startJobRun" ], "Resource": [ "arn:aws:databrew:{{region}}:{{accountId}}:job/*" ] } ] }