> ## Documentation Index
> Fetch the complete documentation index at: https://docs.featureform.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure Blobs

> Featureform supports [Azure Blob Store](https://azure.microsoft.com/en-us/products/storage/blobs/) as an Inference Store and Storage layer for a Kubernetes Offline Store.

## Implementation

An Azure Blob created for every feature. The data type is stored in an index, and the values stored in a keyspace based on their entity. Featureform's scheduler aims to achieve consistency between Azure's internal state with the user's desired state as specified in the metadata service.

## Configuration

First we have to add a declarative Azure Blob configuration in Python.

```py azure_blob_config.py theme={null}
import featureform as ff
azure_blob = ff.register_blob_store(
    name="azure-quickstart",
    description="An azure blob store provider to store offline and inference data" # Optional
    container_name="my_company_container"
    root_path="custom/path/in/container"
    account_name=""
    account_key="" 
)
```

Once our config file is complete, we can apply it to our Featureform deployment

```bash theme={null}
featureform apply azure_blob_config.py --host $FEATUREFORM_HOST
```

We can re-verify that the provider is created by checking the [Providers tab of the Feature Registry](/getting-started/exploring-the-feature-registry).

### Mutable Configuration Fields

* `description`

* `account_secret`

## Kubernetes Offline Store

[Kubernetes serves as a compute layer](/training-offline-stores/kubernetes) for generating training sets, SQL, and Dataframe transformations. To use Kubernetes, a storage layer to store the results of the computation needs to be specified.

```bash azure_blob_config.py theme={null}
import featureform as ff
k8s_store = ff.register_k8s(
    name="k8s",
    description="Native featureform kubernetes compute",
    store=azure_blob,
    team="featureform-team"
)
```