1. Real-time data streaming with AWS Forecast


    Based on your goal, Amazon Forecast can't directly consume data streams. However, a common architecture pattern is to use Amazon Kinesis to capture real-time data streams and then later export the transformed data to S3, which can be consumed by AWS Forecast.

    Here's a basic example of creating a Kinesis stream with Pulumi in Python using the aws.kinesis.Stream resource.

    In this example, we create a Kinesis Stream with 1 shard. Please adjust the shard count according to your expected throughput.

    import pulumi from pulumi_aws import kinesis mypulumi_stream = kinesis.Stream('mypulumi_stream', shard_count=1, tags={ "Environment": "production", "Name": "mypulumi_stream" }) # Export the name of the stream pulumi.export('stream_name', mypulumi_stream.name) # Export the ARN of the stream pulumi.export('stream_arn', mypulumi_stream.arn)

    Please take note that this is only part of the larger architecture to accomplish your goal. After creating this Kinesis stream, you'd have to build a data producer that sends data to the stream, and a data consumer that pushes data to an S3 bucket. The S3 bucket you use as a data source for your AWS Forecast model.

    All of these components are outside Pulumi's scope and would require additional work using AWS SDKs, AWS CLI, or CloudFormation.

    You'll have to look at your specific use-case to wire these stages together as optimal as possible!