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
    fs.create_table(
    name=table_name,
    primary_keys=pk,
    schema=dftopush.schema,
    description=desc
    )

    ## Pushing the data to feature store
    fs.write_table(
        name=table_name,
        df=dftopush,
        mode="overwrite"
    )

• 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...')
push_data_to_feature_store(sparkDF,db_name,table_name,primarykey,desc)
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

Conclusion

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…

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