Hi everyone,

I recently migrated my Great Expectations project from version 1.4.4 to 1.9.0.

In my previous setup, I used S3StoreBackendDefaults to easily configure AWS S3 as the storage backend for my expectations, validation results, checkpoints, and data docs. The code looked like this:

ctx = gx.get_context(
    project_config=DataContextConfig(
        store_backend_defaults=S3StoreBackendDefaults(
            default_bucket_name="...",
            expectations_store_prefix=f"great-expectations/{context_name}/expectations/",
            validation_results_store_prefix=f"great-expectations/{context_name}/uncommitted/validations/",
            validation_definition_store_prefix=f"great-expectations/{context_name}/validation_definitions/",
            checkpoint_store_prefix=f"great-expectations/{context_name}/checkpoints/",
            data_docs_prefix=f"great-expectations/{context_name}/uncommitted/data_docs/",
        )
    ),
)

After reviewing the V1 migration guides and the latest documentation, I noticed that only ephemeral, in-memory, or local filesystem contexts seem to be natively configurable. I am struggling to find an equivalent implementation for S3 in the updated API.

My questions are:

1. Was cloud-specific store backend support (like S3/Azure/GCS) completely removed from GX Core?
2. If so, is the recommended path for cloud-native setups now exclusively centered around GX Cloud, or is there a standard way to achieve the same S3-backed behavior programmatically using GX Core 1.9.x? (e.g., manually syncing local files via boto3 or mount tools like s3fs).

I appreciate any guidance, workarounds, or clarification on the architectural direction of the library regarding cloud metadata storage!

Thank you!