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