Azure Blobs

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=""
    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.

Mutable Configuration Fields

description
account_secret

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"
)

⌘I

Overview

Using Featureform

Featureform Resource Types

LLMs, Embeddings, and Vector Databases

Common Use Cases and Examples

Supported Infrastructure Providers

Deployment

Implementation

Configuration

Mutable Configuration Fields

Kubernetes Offline Store

​Implementation

​Configuration

​Mutable Configuration Fields

​Kubernetes Offline Store

Implementation

Configuration

Mutable Configuration Fields

Kubernetes Offline Store