1. Designing a logistics optimization platform using AWS Lambda, Amazon Aurora, and real-time analytics with Kinesis


    The program consists of four parts:

    Part 1: Set up an AWS RDS Aurora database. This will store your logistics data.

    Part 2: Create an AWS Kinesis Data Stream. This will be used to ingest real-time logistics data.

    Part 3: Create an AWS Lambda function. This function will read data from the Kinesis Stream, process it, and store it in the Aurora database for analytics.

    Part 4: Define an Event Source Mapping to trigger the Lambda function whenever new data is added to the Kinesis Stream.

    Here is the pulumi program:

    import * as pulumi from "@pulumi/pulumi"; import * as aws from "@pulumi/aws"; // Part 1: Set up an AWS Aurora Database const cluster = new aws.rds.Cluster("logistics-cluster", { engine: "aurora", // We're using Amazon Aurora masterUsername: "<username>", masterPassword: "<password>", skipFinalSnapshot: true, }); const clusterInstance = new aws.rds.ClusterInstance("instance", { clusterIdentifier: cluster.clusterIdentifier, instanceClass: "db.r4.large", engine: "aurora", }); // Part 2: Create an AWS Kinesis Stream const kinesisStream = new aws.kinesis.Stream("logistics-stream", { shardCount: 1, // The number of shards for the stream. Adjust as necessary }); // Part 3: Create an AWS Lambda function const lambdaRole = new aws.iam.Role("lambdaRole", { assumeRolePolicy: JSON.stringify({ Version: "2012-10-17", Statement: [ { Action: "sts:AssumeRole", Principal: { Service: "lambda.amazonaws.com", }, Effect: "Allow", Sid: "", }, ], }), }); new aws.iam.RolePolicyAttachment("lambdaRolePolicyAttachment", { role: lambdaRole.name, policyArn: aws.iam.ManagedPolicy.AWSLambdaFullAccess, // Change this as per your policy requirements }); const lambdaFunction = new aws.lambda.Function("lambdaFunction", { code: new pulumi.asset.FileArchive("lambda-handler.zip"), // Path to your lambda function code runtime: aws.lambda.NodeJS12dXRuntime, role: lambdaRole.arn, handler: "lambda-handler.handler", // Your lambda function entrypoint i.e. filename.methodname }); // Part 4: Event Source Mapping new aws.lambda.EventSourceMapping("lambdaEventSourceMapping", { eventSourceArn: kinesisStream.arn, functionName: lambdaFunction.arn, startingPosition: "TRIM_HORIZON", }); // Exports export const dbEndpoint = clusterInstance.endpoint; export const kinesisStreamName = kinesisStream.name; export const lambdaFunctionName = lambdaFunction.name;

    Notes & Assumptions

    1. You'll need to replace the placeholder values for <username>, <password>, and the path to your lambda function handler file code in the aws.lambda.Function creation.

    2. This setup uses NodeJS runtime for AWS Lambda. If you're using a different runtime, please update aws.lambda.NodeJS12dXRuntime and handler: "lambda-handler.handler" to reflect your actual setup.

    3. IAM Policies and instance classes are placeholders. Modify them based on your requirements.

    4. This is a base setup. For a production setup, consider enabling encryption, setting up backups, and adjusting capacity based on expected load.

    For more details, refer to the respective pages for aws.lambda.Function, aws.rds.Cluster, aws.rds.ClusterInstance and aws.kinesis.Stream