Learn how to create EMR runtime roles to ensure each team’s big data workloads can only access the necessary AWS services within an Amazon EMR cluster, without granting access to IMDSv2 on the underlying EC2 instances.
Table of Contents
Question
A company wants to create an Amazon EMR cluster that multiple teams will use. The company wants to ensure that each team’s big data workloads can access only the AWS services that each team needs to interact with. The company does not want the workloads to have access to Instance Metadata Service Version 2 (IMDSv2) on the cluster’s underlying EC2 instances.
Which solution will meet these requirements?
A. Configure interface VPC endpoints for each AWS service that the teams need. Use the required interface VPC endpoints to submit the big data workloads.
B. Create EMR runtime roles. Configure the cluster to use the runtime roles. Use the runtime roles to submit the big data workloads.
C. Create an EC2 IAM instance profile that has the required permissions for each team. Use the instance profile to submit the big data workloads.
D. Create an EMR security configuration that has the EnableApplicationScopedIAMRole option set to false. Use the security configuration to submit the big data workloads.
Answer
B. Create EMR runtime roles. Configure the cluster to use the runtime roles. Use the runtime roles to submit the big data workloads.
Explanation
The correct solution to meet the company’s requirements is to create EMR runtime roles, configure the cluster to use the runtime roles, and use the runtime roles to submit the big data workloads.
EMR runtime roles allow you to define fine-grained permissions for individual jobs or queries running on an EMR cluster. By creating separate runtime roles for each team, you can tailor the permissions to grant access only to the specific AWS services that each team needs to interact with. This ensures that the big data workloads are limited to accessing only the necessary resources.
To implement this solution:
- Create IAM roles for each team, specifying the permissions required to access the specific AWS services they need.
- Configure the EMR cluster to use these IAM roles as runtime roles. This can be done by specifying the runtime roles during cluster creation or by modifying the cluster configuration.
- When submitting the big data workloads, specify the appropriate runtime role for each team’s workload. This ensures that the workloads run with the permissions defined in the associated runtime role.
By using EMR runtime roles, you can achieve granular access control for each team’s workloads, limiting their access to only the required AWS services. Additionally, since the permissions are defined at the workload level rather than the instance level, the workloads will not have access to the Instance Metadata Service Version 2 (IMDSv2) on the underlying EC2 instances.
The other options mentioned are not suitable for this scenario:
- Option A (using interface VPC endpoints) does not provide the necessary granular access control for each team’s workloads.
- Option C (using an EC2 IAM instance profile) would grant permissions at the instance level, which is not desirable in this case and would also allow access to IMDSv2.
- Option D (using an EMR security configuration with EnableApplicationScopedIAMRole set to false) is not relevant to the specific requirements mentioned.
Therefore, creating EMR runtime roles, configuring the cluster to use them, and using the runtime roles to submit the big data workloads is the best solution to meet the company’s requirements.
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