Enabling AWS S3#

For Union customers whose data plane is in AWS, we walk through setting up access to your own AWS S3 bucket.

AWS S3 in the Union environment

Your data plane is set up with a Kubernetes cluster and other resources. Among these are a number of S3 buckets used internally by the Union operator running in the cluster (see Platform architecture) to store things like workflow metadata.

These are not the S3 bucket we are talking about in this section.

We are discussing the case where you have your own S3 bucket that you set up to store input and output data used by your workflows.

Add permissions to your custom policy#

In order to enable access to an AWS resource (in this case S3) you need to create a custom policy in AWS IAM with the required permissions and attach it to either the existing User Flyte Role associated with your data plane Kubernetes cluster or to a custom role which you have created and attached to the cluster. The general procedure is covered in Enabling AWS resources).

In order to enable S3 access in particular, in the step #add-permissions-to-your-custom-policy you must specify the needed permissions. For example:

  • s3:ListBucket - This permission allows you to list the objects in the bucket.

  • s3:GetObject - This permission allows you to retrieve objects from the bucket.

  • s3:PutObject - This permission allows you to upload objects to the bucket.

Here is a sample JSON policy document that grants these permissions:

  "Version": "2012-10-17",
  "Statement": [
      "Sid": "AllowReadWriteBucket",
      "Effect": "Allow",
      "Action": [
      "Resource": [

In the Resource field, replace <BucketName> with the actual name of your S3 bucket.

Accessing S3 from your task code#

Once you have enabled access to your S3 bucket, you can use the standard AWS SDK for Python (Boto3) in your task code to read and write to it.