Easiest way to Create a Databricks Feature Store – 2023

Hey guys, in this blog we will see how we can create a Databricks Feature Store. This is going to be a very interesting blog, so without any further due, let’s do it…

Creating a Databricks Feature Store involves setting up a centralized repository for storing and managing feature data used in machine learning and data analytics. Databricks Feature Store simplifies the process of storing, accessing, and sharing feature data across your organization.

We will open a Workspace Notebook and follow the following steps to create a Databricks feature store.

Step 1 – Installing and Importing the required dependencies.

## Installing required libraries
!pip install fsspec
!pip install s3fs 
!pip install dask

## Importing required libraries
import boto3
import pandas as pd
from databricks.feature_store import feature_table
from databricks.feature_store import FeatureStoreClient
  • Before creating a Databricks feature store, we must install some dependencies by running the above cell in the Databricks Workspace Notebook.

Step 2 – Defining the Feature Engineering function

## feature engineering function
def feature_eng(data):
    return data
  • In this example, I am not doing any Feature Engineering on the data.
  • If you need to do any Feature Engineering you can write the code here.

Step 3 – Function to create data

def create_data():
    ## Reading data
    df = pd.read_parquet('path/to/input/parquet/file')

    #feature engineering
    maindf = feature_eng(maindf)
    print('Feature Engineering Done...')

    #converting to Spark Dataframe
    print('Creating Spark Dataframe...')
    sparkDF = spark.createDataFrame(maindf) 
    print('Spark Dataframe Created...')

    return sparkDF
  • The create_data function will read the input data from the source.
  • That data will be passed to the feature_eng function we defined above to perform feature engineering on that dataframe.
  • Finally, that feature-engineered pandas dataframe will be converted to a spark dataframe.

Step 4 – Function to Create a Databricks Feature Store

## This function will push the spark dataframe to feature store
def push_data_to_feature_store(dftopush,db_name,table_name,pk,desc):
    ## Initializing Feature Store Client
    fs = FeatureStoreClient()

    ## command to create database
    spark.sql(f"CREATE DATABASE IF NOT EXISTS {db_name}") 

    ## Creating the table in feature_store

    ## Pushing the data to feature store
  • The push_data_to_feature_store function will simply take the dataframe that we need to push to the feature store, database name, table name, primary key in the dataframe, and description of the features.
  • In the function, we have initialized the Feature Store Client that will help us communicate with the Feature Store.
  • Then we will run an SQL command to create the database in the catalog if it doesn’t exist.
  • Then we will simply create the table using the create_table function and write to it using the write_table function.

Step 5 – Let’s create data and push it to the Feature Store

## Creating Data
print('Creating Data...')
sparkDF = create_data()
print('Data Created Successfully...')

## defining db name, table name, and primarykey
db_name = 'your-db-name'
table_name = f'{db_name}.table-name'
primarykey = ['primary-key']
desc = 'Dummy Description'

## Pushing to Feature Store
print('Pushing Data to Feature Store...')
print('Data pushed successfully to feature store...')
  • Here we are simply calling all the functions we declared above.
  • First of all, we will call the create_data() function that will create our spark dataframe and return it. We will call this returned df sparkDF.
  • Now we will define some variables like db_name (database name), table_name, primarykey, and description. You need to change these values according to you.
  • Now we will finally call the push_data_to_feature_store() and pass all these values to push our dataframe to the Feature Store.

Feature Store Snapshot

Create a Databricks Feature Store


Remember that the specific details and features of Databricks Feature Store may evolve over time, so it’s essential to refer to the most up-to-date documentation and resources provided by Databricks for the latest information.

So in this way, you can create a Databricks feature store. This was all for this blog guys, hope you enjoyed it…


What is a Databricks Feature Store?

A Databricks Feature Store is a centralized repository for storing, managing, and serving feature data for machine learning and analytics applications. It enables data scientists and engineers to easily access and share feature data for building and deploying machine learning models.

Why do I need a Feature Store in Databricks?

A Feature Store simplifies feature engineering, promotes reusability of features, and ensures consistency across different parts of your organization. It helps streamline the machine learning pipeline and accelerates model development and deployment.

How do I create a Databricks Feature Store?

You can create a Databricks Feature Store by using the Databricks Feature Store API and the Databricks Runtime environment. You’ll need to define feature groups, ingest data, and manage metadata using this API.

What are Feature Groups in a Databricks Feature Store?

Feature Groups are collections of features related to a specific entity or use case. They are organized for easy retrieval and sharing. You can create, version, and maintain Feature Groups within the Feature Store.

Can I use external data sources with Databricks Feature Store?

Yes, Databricks Feature Store supports integrating with external data sources such as data lakes, databases, and streaming data platforms, allowing you to ingest and manage feature data from various sources.

How do I serve feature data from Databricks Feature Store to machine learning models?

Databricks Feature Store provides APIs to serve feature data directly to your machine learning models in Databricks or other environments. You can easily access and use the features in your model training and prediction processes.

Is there a versioning system in the Databricks Feature Store?

Yes, Databricks Feature Store supports versioning of Feature Groups and feature data. This helps in tracking changes and ensuring consistency when you update or modify features.

Can I monitor and track the usage of features in the Databricks Feature Store?

Yes, Databricks Feature Store provides monitoring and tracking capabilities, allowing you to understand how features are used in your machine learning pipelines. You can monitor feature distribution, data quality, and performance.

What are some best practices for managing a Databricks Feature Store?

Best practices include defining clear naming conventions, versioning feature groups, documenting metadata, implementing access controls, and regularly monitoring and maintaining the feature store to ensure data quality and consistency.

Is Databricks Feature Store suitable for both batch and streaming data processing?

Yes, Databricks Feature Store is designed to support both batch and streaming data processing. You can ingest and serve feature data for both types of workloads.

Does Databricks Feature Store support data lineage and auditing?

Yes, Databricks Feature Store provides data lineage and auditing capabilities, helping you trace the origin of feature data and track changes made to it over time.

How can I get started with Databricks Feature Store?

To get started, you can refer to the Databricks documentation, which includes tutorials and guides on setting up and using the Feature Store within the Databricks platform.

Read my last article – Exploring GFPGAN for Cutting-Edge Image Generation – with project

Check out my other machine learning projectsdeep learning projectscomputer vision projectsNLP projectsFlask projects at machinelearningprojects.net

Abhishek Sharma
Abhishek Sharma

Started my Data Science journey in my 2nd year of college and since then continuously into it because of the magical powers of ML and continuously doing projects in almost every domain of AI like ML, DL, CV, NLP.

Articles: 521

Leave a Reply

Your email address will not be published. Required fields are marked *