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Sai Sharan Tangeda

Sai Sharan Tangeda


Managed Feature Store: Improving data reusability & Providing a means for low latency real-time prediction at Udaan

Submitted Jul 11, 2021

A brief talk on Managed Feature Store built on top of Open Source Feast. We will start with a brief walkthrough of the open source Feast feature store including the architecture and core capabilities. We would call out some of the challenges/limitations of the open source Feast feature store. We would then describe some of the enhancements which enables us to have a more robust, secure and scalable deployment by using a) managed resources on Cloud platforms for eg, Kafka vs Event Hub (Azure), open source Spark vs Databricks; b) Integration of RBAC & Table Level Access Control to maintain controlled usage c) Scalable batch ingestion by using Spark instead of Pandas & addition of new capabilities to increase data reusability.

Dr Mohit Kumar (Head - Data Science, Product Analytics and Data Platform)
Sai Sharan Tangeda (Data Scientist)
Time: 30 mins


  1. Introduction
    1. Introduction
    2. Motivation for maintaining a Managed Feature Store
  2. Feast (Open Source): Constructs, Core Capabilities & Limitation
    1. Constructs & Architecture of Feast
    2. Point In Time Join Capabilities with Batch Retrieval
    3. Batch Ingestion into Historical Store & Scale Limitations
    4. Streaming Capabilities with Apache Kafka & Redis
    5. Reliability issues with self deployed resources like Kafka, Redis, PostgreSQL
  3. Managed Feature Store as a fork of Feast
    1. Overview of Core Architecture
    2. Integration of Azure Eventhubs as a replacement for Apache Kafka
    3. Introducing Databricks as Spark Backend
    4. Ensuring Scalability for large data sizes via Spark
    5. RBAC & Table Level Access Control for controlled usage
    6. End-to-End flow for real-time model serving
  4. Closing Arguments
    1. Increase in Productivity with ready-to-use Features

Link to slides:


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