1. Data warehousing with AWS Kinesis


    Sure, I'll help you create a simple Pulumi program for data warehousing with AWS Kinesis. In essence, we will create an AWS Kinesis Stream that can receive and store data records. Please note that the data processing will not be shown; that's a bit more complex and falls outside the scope of this goal.

    Here is the Pulumi program:

    import * as pulumi from "@pulumi/pulumi"; import * as aws from "@pulumi/aws"; // Create an AWS Kinesis Stream const kinesisStream = new aws.kinesis.Stream("myStream", { shardCount: 2, // Define the number of shards for your stream retentionPeriod: 24, // Data records are accessible for this many hours after they are added to the stream. tags: { Environment: "test" } }); // Export the name and ARN of the Kinesis Stream export const streamName = kinesisStream.name; export const streamArn = kinesisStream.arn;

    The aws.kinesis.Stream object defines a Kinesis Data Stream. In this example, the stream has two shards (shardCount: 2) which define the stream's capacity for ingesting and exporting data. The retentionPeriod is set to 24 hours, meaning data records are accessible for 24 hours after they are added to the stream. Additionally, the stream is tagged with Environment: test.

    Please modify this code to better suit your data warehousing requirements. Remember also to configure and authenticate your AWS environment properly to allow Pulumi to manage resources there.

    Keep in mind that while this gives you the capability to ingest data into AWS Kinesis, you will still need additional infrastructures, such as AWS Lambda or EC2 instances to process the data in the Kinesis Stream or even AWS Redshift for long-term data warehousing. Furthermore, you might want to investigate AWS Glue for extracting, transforming, and loading (ETL) your data into Redshift.

    We suggest studying use cases and AWS resource properties deeper to optimize your data warehousing solution.