1. Interactive Data Exploration for AI Using EMR Serverless


    Pulumi provides a way to create, deploy, and manage cloud infrastructure using code, which is known as Infrastructure as Code (IaC). For setting up an interactive data exploration environment for AI using EMR Serverless on AWS with Pulumi, you'd typically proceed as follows:

    1. Create an EMR Serverless Application: This is an AWS service that allows you to run big data analytics applications using open-source analytics frameworks without managing clusters or servers. EMR Serverless is ideal for interactive data analysis and supports a pay-as-you-go pricing model.

    2. Configure the Application: You must define the type of application, release label, and resource configurations like CPU, memory, and disk. You can also set auto-stop configurations to automatically stop the application if it's idle, helping you save costs.

    3. Set Up Networking: You should configure networking for your EMR Serverless Application, providing subnet IDs and security group IDs so that your application can communicate within your VPC.

    4. Monitoring: Optionally, you can configure monitoring to send logs to an S3 bucket for later analysis or to monitor the application performance.

    5. IAM Roles: You'll need to set up the necessary IAM roles and policies that grant your EMR Serverless Application the permissions needed to access other AWS resources.

    Now, here's a Pulumi Python program that will set up an EMR Serverless application. This program uses the aws.emrserverless.Application Pulumi resource, which corresponds to the Amazon EMR Serverless Application service.

    The program will provision the following AWS resources:

    • EMR Serverless Application: The main resource that defines your EMR Serverless workspace.
    • IAM Roles: Roles that allow EMR Serverless to access other AWS services on your behalf.
    • S3 Bucket: For storing logs and output data.
    import pulumi import pulumi_aws as aws # Define an S3 bucket to store logs logs_bucket = aws.s3.Bucket("emr-serverless-logs") # IAM Role for EMR Serverless - make sure the role has the necessary permissions emr_role = aws.iam.Role("emr-serverless-role", assume_role_policy=aws.iam.get_policy_document(statements=[ aws.iam.GetPolicyDocumentStatementArgs( actions=["sts:AssumeRole"], principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs( type="Service", identifiers=["emr-serverless.amazonaws.com"], )], ), ]).json, ) # Attach the necessary policies to the role aws.iam.RolePolicyAttachment("emr-serverless-AmazonS3ReadOnlyAccess", policy_arn="arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess", role=emr_role.name, ) # Create EMR Serverless application emr_serverless_app = aws.emrserverless.Application("emr-serverless-app", type="SPARK", # or use "HIVE" depending on your use case release_label="emr-6.3.0-latest", # choose an appropriate release label initial_capacity=[{ "initial_capacity_type": "Driver", "initial_capacity_config": { "worker_count": 1, "worker_configuration": { "cpu": "4 vCPU", "memory": "16 GB", }, }, }], maximum_capacity={ "cpu": "16 vCPU", "memory": "64 GB", }, auto_start_configuration={ "enabled": True }, worker_type_specifications={"cpu_spec":{"cpu":"Cpu Type"}}, network_configuration={ "subnet_ids": ["subnet-xxxxxxxxxxx"], # replace with your subnet ID "security_group_ids": ["sg-xxxxxxxxxx"], # replace with your security group ID }, monitoring_configuration={ "s3_monitoring_configuration": { "log_uri": pulumi.Output.concat("s3://", logs_bucket.id, "/logs/"), }, }, # Adding the role defined above for access to resources role_arn=emr_role.arn, ) # Export the EMR Serverless Application ID pulumi.export("emr_serverless_application_id", emr_serverless_app.id) # Export the URL to the logs S3 bucket pulumi.export("logs_bucket_url", logs_bucket.bucket_regional_domain_name.apply( lambda domain_name: f"https://{domain_name}") )

    This program will perform the following actions:

    • It creates a new S3 bucket to store logs from the EMR Serverless application.
    • It defines an IAM role that the EMR service will assume to perform actions on your behalf.
    • It attaches an AmazonS3ReadOnlyAccess policy to the IAM role, which allows EMR Serverless to read data from S3.
    • It provisions an EMR Serverless application with a specified configuration. In this case, we're setting up for a Spark application, but you can change that to Hive or others as needed.
    • It exports the ID of the EMR Serverless application and the S3 bucket domain name, so you can quickly access these after deployment.

    Before running this program, ensure you have the Pulumi CLI installed and configured for use with your AWS account. You'll also need Python 3 installed to run the Pulumi program. Save the program in a file, __main__.py, in a directory with a Pulumi.yaml file that specifies the Python runtime, and run pulumi up to deploy the resources.