Enforcing Data Retention with DLM Lifecycle Rules

Question

Pulumi · Accepted Answer

Data Lifecycle Management (DLM) is an important aspect of managing resources in cloud environments, ensuring that data is retained according to compliance and business requirements, and that unnecessary data is deleted to save costs.

AWS offers lifecycle policies for various services like Amazon S3 (Simple Storage Service) and EBS (Elastic Block Store), allowing you to automate the transition of data to different storage classes and the deletion of old data. In the Pulumi ecosystem, you can implement DLM lifecycle rules using AWS resources such as `aws.s3.Bucket` and `aws.ebs.Volume`. Below is an example that demonstrates how to apply a lifecycle policy to an S3 bucket using Pulumi and TypeScript.

This program will create an S3 bucket and attach a lifecycle policy that specifies how objects in the bucket are managed over time. The lifecycle rule in the following example will do the following:

- Transition objects to the STANDARD_IA (Standard-Infrequent Access) storage class 30 days after creation.
- Transition objects to the GLACIER storage class 60 days after creation.
- Permanently delete objects 365 days after creation.

Here's the TypeScript program:

```typescript
import * as aws from "@pulumi/aws";

// Create a new S3 bucket
const bucket = new aws.s3.Bucket("my-bucket", {
    // Bucket settings and properties go here.
});

// Add a lifecycle rule to the bucket
const lifecyclePolicy = new aws.s3.BucketLifecycleConfiguration("my-bucket-lifecycle", {
    bucket: bucket.bucket, // Reference to the bucket created above
    rules: [{
        enabled: true,
        id: "log",
        prefix: "log/", // This lifecycle rule applies to objects with the prefix "log/"
        tags: {
            "autoclean": "true",
            "rule": "log",
        },
        transitions: [
            {
                // Transition to STANDARD_IA (Standard - Infrequent Access) after 30 days
                days: 30,
                storageClass: "STANDARD_IA",
            },
            {
                // Transition to GLACIER after 60 days
                days: 60,
                storageClass: "GLACIER",
            },
        ],
        expiration: {
            // Specify expiration (permanent deletion) for objects after 365 days
            days: 365,
        },
    }],
});

// Export the name of the bucket
export const bucketName = bucket.id;

// Export the bucket's region
export const bucketRegion = bucket.region;
```

In the above program, we:

1. Import the necessary AWS module from the Pulumi library.
2. Create an S3 bucket where our data will be stored.
3. Define a lifecycle policy using `aws.s3.BucketLifecycleConfiguration` that includes a set of rules that match our retention requirements.
4. Export the bucket name and region for easy access or for use in further stack configurations.

The lifecycle configuration adheres to the bucket we've created by referencing its `bucket` property. The rule set under `rules` includes transitions to different storage classes and an expiration action, all coming with an associated timeframe defined in days.

Remember, you need to have AWS credentials configured in your environment, and Pulumi CLI installed to run this program. When the program runs, Pulumi will issue the appropriate calls to AWS to create these resources with the specified attributes.