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Azure Blobs

Featureform supports Azure Blob Store 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.
azure_blob_config.py
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="<azure_account_name>"
account_key="<azure_account_key>"
)
Once our config file is complete, we can apply it to our Featureform deployment
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.

Kubernetes Offline Store

Kubernetes serves as a compute layer 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.
azure_blob_config.py
import featureform as ff
k8s_store = ff.register_k8s(
name="k8s",
description="Native featureform kubernetes compute",
store=azure_blob,
team="featureform-team"
)