Orchestrating Data Lifecycle Policies via AWS S3 Inventory

Question

Pulumi · Accepted Answer

### Understanding AWS S3 Inventory and Data Lifecycle Policies

When working with AWS S3, managing a large number of objects can be challenging. AWS S3 Inventory provides a way to list your objects and their corresponding metadata for an S3 bucket or a shared prefix. This becomes especially useful for maintaining, analyzing, and orchestrating the lifecycle of your data.

A data lifecycle policy, on the other hand, is a set of rules defining actions on objects that meet certain criteria. For example, you might want to automatically archive files to a cheaper storage class after they haven't been accessed for 30 days, or you might want to permanently delete objects that are older than a year.

Using Pulumi, you can declare these resources in code, which provides a repeatable and versionable way to manage your cloud infrastructure. Below, we will write a Pulumi program in Python that creates an S3 bucket with an inventory configuration. The information provided by this inventory can then be used to setup lifecycle policies based on your business needs.

### Pulumi Program for AWS S3 Inventory and Data Lifecycle Policies

```python
import pulumi
import pulumi_aws as aws

# Create an S3 bucket that will store the inventory.
inventory_bucket = aws.s3.Bucket("inventoryBucket")

# Create an S3 bucket that the inventory will cover.
data_bucket = aws.s3.Bucket("dataBucket")

# Define inventory configuration for the data bucket.
inventory_configuration = aws.s3.BucketInventory("inventoryConfiguration",
    bucket=data_bucket.id,
    included_object_versions="All",
    schedule={ "frequency": "Daily" },
    destination={
        "bucket": {
            "format": "ORC",
            "bucket_arn": inventory_bucket.arn,
            "prefix": "inventory"
        }
    },
    optional_fields=["Size", "LastModifiedDate", "StorageClass"]
)

# Define a lifecycle rule to manage objects in the data bucket.
# For example, transition objects to Glacier after 90 days, and expire them after 1 year.
lifecycle_rule = aws.s3.BucketLifecycleConfiguration("lifecycleRule",
    bucket=data_bucket.id,
    rules=[{
        "id": "transitionAndExpire",
        "enabled": True,
        "filter": {
            "prefix": "documents/"
        },
        "transitions": [{
            "days": 90,
            "storageClass": "GLACIER"
        }],
        "expiration": {
            "days": 365
        }
    }]
)

# Export the names of the buckets
pulumi.export("inventory_bucket_name", inventory_bucket.id)
pulumi.export("data_bucket_name", data_bucket.id)
```

### Explanation of the Program

- We import Pulumi and the AWS package that allows us to work with AWS resources in Python.
- Two S3 buckets are created:
  - `inventory_bucket`: This bucket is where the S3 Inventory results will be stored.
  - `data_bucket`: This is the bucket for which we want to set up the inventory and lifecycle rule.
- We define an inventory configuration for the `data_bucket` that specifies:
  - Which bucket the inventory applies to (`bucket=data_bucket.id`).
  - The frequency of inventory reports (daily in this case).
  - The format and destination of the inventory listing (`bucket_arn=inventory_bucket.arn`).
  - Additional optional fields to include in the inventory reports.
- We add a lifecycle rule to the `data_bucket` that specifies:
  - A prefix filter to apply the rule to objects under the 'documents/' prefix.
  - A transition action that moves objects to the Glacier storage class after 90 days.
  - An expiration action that deletes objects after 1 year (365 days).
- Finally, we export the names of the buckets so they can be accessed outside the Pulumi program.

This program automates the setup of S3 Inventory and lifecycle policies on your AWS account. It is a clear, declarative approach to manage your S3 data and can be integrated into your CI/CD workflow for infrastructure deployment.