1. Object Storage with Hetzner Cloud Storage Boxes for Datasets


    Object storage is a versatile method for storing large amounts of unstructured data. Hetzner Cloud Storage Boxes can provide a scalable and secure solution for hosting datasets. In the context of Pulumi, there isn't an existing Hetzner Cloud provider, but you can use Pulumi to manage cloud infrastructures that support Object Storage, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.

    Since Hetzner Cloud currently doesn't have an integrated provider with Pulumi, I am unable to provide a Pulumi script for Hetzner Cloud Storage Boxes. However, for the purpose of illustration and learning, I can show you how to create similar resources using AWS S3, which provides a reliable and widely-used object storage service.

    In this example, you'll see how to create an S3 Bucket using Pulumi with AWS, which acts as object storage similar to Hetzner Cloud Storage Boxes. We will also create a bucket object, which represents a dataset that you might upload to your object storage.

    The resources used in this example include:

    1. aws.s3.Bucket: This resource creates a new S3 Bucket that will hold your data objects (datasets).
    2. aws.s3.BucketObject: This resource is used to upload a data object (like a dataset) to the S3 Bucket.

    Here's a Pulumi script in Python that demonstrates creating an S3 Bucket and uploading a sample dataset to it:

    import pulumi import pulumi_aws as aws # Create an AWS S3 bucket to serve as object storage for datasets. bucket = aws.s3.Bucket('dataset-storage', acl='private' # Access control list set to private. It could be 'public-read' if you want it to be publicly accessible. ) # Upload a sample dataset to the S3 bucket. # Replace 'path/to/dataset.csv' with the actual file path to your dataset. dataset = aws.s3.BucketObject('sample-dataset', bucket=bucket.id, # Reference to our bucket's ID. key='dataset.csv', # The name of the file that will appear in the bucket. source=pulumi.FileAsset('path/to/dataset.csv') # The local path to the dataset file. ) # Export the name of the bucket and the URL of the uploaded dataset. pulumi.export('bucket_name', bucket.id) pulumi.export('dataset_url', pulumi.Output.all(bucket.bucket_regional_domain_name, dataset.key).apply( lambda args: f"https://{args[0]}/{args[1]}")) # Construct the dataset's URL within the bucket.

    This script creates an S3 Bucket in your AWS environment, uploads a dataset, and then provides an output URL for the uploaded dataset.

    Please remember to replace 'path/to/dataset.csv' with the actual file path of your dataset that you want to upload.

    To use this Pulumi script:

    1. Ensure you have Pulumi installed and configured with AWS credentials.
    2. Save this script into a __main__.py file inside a Pulumi project directory.
    3. Run pulumi up from the command line in the project directory to execute the script and create the resources in AWS.

    Remember that this script is for AWS S3 and meant to illustrate how you could manage object storage using Pulumi. If you need to work specifically with Hetzner Cloud Storage Boxes, you will likely need to interact with Hetzner's APIs using a custom script until a Pulumi provider becomes available.