1. Real-time Data Ingestion for Machine Learning with AWS Kinesis Firehose


    To accomplish real-time data ingestion for machine learning purposes, AWS provides a managed service called Kinesis Firehose. This service allows you to load streaming data into AWS's data stores such as S3, Redshift, Elasticsearch Service, and others. The streaming data can then be used for analytics and machine learning tasks.

    In Pulumi, we'd use the aws package to create and manage the Kinesis Firehose stream. Specifically, we would create an instance of aws.kinesis.FirehoseDeliveryStream. This delivery stream would be configured based on where you want to load your data and how. For instance, if you want to perform real-time analytics on the stream, you could configure the stream to load into AWS S3 and then use AWS Glue or Amazon Athena to query the data.

    Let's walk through a basic example where we create a Kinesis Firehose Delivery Stream that delivers data into an AWS S3 bucket, suitable for further processing and analysis. This data can then be utilized by machine learning models or analytics tools.

    Below is a Pulumi program written in Python that sets up such a pipeline:

    import pulumi import pulumi_aws as aws # Create an AWS S3 bucket to store the data ingested by Kinesis Firehose. s3_bucket = aws.s3.Bucket("s3BucketForFirehoseData") # Create an AWS IAM Role for the Firehose service to access resources like S3 on behalf of your delivery stream. firehose_iam_role = aws.iam.Role("firehoseIamRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "firehose.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""" ) # Attach a policy to the IAM Role that allows it to put data into the S3 bucket. firehose_iam_role_policy = aws.iam.RolePolicy("firehoseIamRolePolicy", role=firehose_iam_role.id, policy=s3_bucket.arn.apply(lambda arn: """{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:AbortMultipartUpload", "s3:GetBucketLocation", "s3:GetObject", "s3:ListBucket", "s3:ListBucketMultipartUploads", "s3:PutObject" ], "Resource": [ "%s/*", "%s" ] } ] }""" % (arn, arn)) ) # Create a Kinesis Firehose Delivery Stream that loads data into the S3 bucket. firehose_delivery_stream = aws.kinesis.FirehoseDeliveryStream("firehoseDeliveryStream", destination="s3", s3_configuration=aws.kinesis.FirehoseDeliveryStreamS3ConfigurationArgs( role_arn=firehose_iam_role.arn, bucket_arn=s3_bucket.arn, ) ) # Export the name of the bucket and the ARN of the delivery stream. pulumi.export("s3_bucket_name", s3_bucket.bucket) pulumi.export("firehose_delivery_stream_arn", firehose_delivery_stream.arn)

    In this program:

    • We start by creating an S3 bucket where the ingested data will be stored (s3_bucket).
    • Next, we set up an IAM role that Firehose will assume to get the necessary permissions to put data into the created S3 bucket (firehose_iam_role).
    • We attach an IAM policy to the role that provides access to the S3 bucket (firehose_iam_role_policy).
    • We define the Kinesis Firehose Delivery Stream and configure it to use the IAM role and to store data into the S3 bucket (firehose_delivery_stream).
    • Finally, we export the names of the S3 bucket and the ARN of the Firehose Delivery Stream so that they can be easily referenced later.

    With this setup, you're ready to start sending streaming data to the Firehose Delivery Stream. This data will be automatically loaded into the specified S3 bucket in near-real-time, and from there, you can use various AWS services for data analysis, processing, and machine learning.