1. Financial data analytics using AWS Personalize

    Python

    Sure, here is a basic Pulumi Python program for setting up AWS Personalize for financial data analytics.

    This program sets up AWS Personalize with a Schema, a Dataset, and a Solution. Note that you'll need to provide your own AWS Roles, Bucket, and data in S3 for this to be functional.

    You should replace the placeholders in the code with your own values where mentioned.

    import pulumi
from pulumi_aws_native import personalize

# Define the Schema for personalized recommendations
personalize_schema = personalize.Schema(
    "finance_data",
    schema='<Your Avro Schema representing financial data>',  # replace with your schema
    name="finance_data",
)

# Define the dataset using the defined schema
personalize_dataset = personalize.Dataset(
    "dataset",
    dataset_type='Interactions', 
    dataset_group_arn='<ARN of your dataset group>',  # replace with your dataset group ARN
    schema_arn=personalize_schema.arn,
)

# Start a dataset import job that fetches the data from the S3
dataset_import_job = personalize.DatasetImportJob(
    "dataset_import_job",
    job_name="finance_data_import",
    role_arn='<Role ARN with S3 read access>',  # replace with your own IAM Role ARN with access to S3
    dataset_arn=personalize_dataset.arn,
    datasource={"dataLocation": "s3://<path to your data in an S3 bucket>"},  # replace with the S3 location of your financial data
)

# Define a Solution based on a predefined recipe
personalize_solution = personalize.Solution(
    "finance_recommendation_engine",
    name="finance_recommendation_engine",
    dataset_group_arn='<ARN of your dataset group>',  # replace with your dataset group ARN
    perform_auto_ml=True,
)

# Export ARNs of created resources
pulumi.export("schemaArn", personalize_schema.arn)
pulumi.export("datasetArn", personalize_dataset.arn)
pulumi.export("datasetImportJobArn", dataset_import_job.arn)
pulumi.export("solutionArn", personalize_solution.arn)

    Check the following Pulumi Registry documentation links for more details:

    Be sure to have your Avro Schema representing financial data handy, and replace the placeholders with specific values related to your AWS account and S3 data location.

    Remember, Amazon Personalize uses machine learning to generate personalized recommendations and needs sufficient data to provide meaningful results. Also, data importing and model training may take a while depending on the amount of data and the complexity of the solution.


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