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  • Introduction
  • Quickstart
  • Getting started
    • Install Feast
    • Create a feature repository
    • Deploy a feature store
    • Build a training dataset
    • Load data into the online store
    • Read features from the online store
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  • Concepts
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    • Data sources
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    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
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        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
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      • Learn Feast
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      • Minimal Ride Hailing Example
    • User guide
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      • Getting online features
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    • Advanced
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    • Contribution process
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On this page
  • Backlog
  • Scheduled for development (next 3 months)
  • Feast 0.10
  • New Functionality
  • Technical debt, refactoring, or housekeeping
  • Feast 0.9
  • New Functionality
  • Feast 0.8
  • New Functionality
  • Technical debt, refactoring, or housekeeping
  • Feast 0.7
  • New Functionality
  • Technical debt, refactoring, or housekeeping
  • Feast 0.6
  • New functionality
  • Technical debt, refactoring, or housekeeping
  • Feast 0.5
  • New functionality
  • Technical debt, refactoring, or housekeeping

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Roadmap

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Last updated 3 years ago

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Backlog

  • Add On-demand transformations support

  • Add Data quality monitoring

  • Add Snowflake offline store support

  • Add Bigtable support

  • Add Push/Ingestion API support

Scheduled for development (next 3 months)

  • Ensure Feast Serving is compatible with the new Feast

    • Decouple Feast Serving from Feast Core

    • Add FeatureView support to Feast Serving

    • Update Helm Charts (remove Core, Postgres, Job Service, Spark)

  • Add Redis support for Feast

  • Add direct deployment support to AWS and GCP

  • Add Dynamo support

  • Add Redshift support

Feast 0.10

New Functionality

  1. Full local mode support (Sqlite and Parquet)

  2. Provider model for added extensibility

  3. Firestore support

  4. Native (No-Spark) BigQuery support

  5. Added support for object store based registry

  6. Add support for FeatureViews

  7. Added support for infrastructure configuration through apply

Technical debt, refactoring, or housekeeping

  1. Remove dependency on Feast Core

  2. Feast Serving made optional

  3. Moved Python API documentation to Read The Docs

Feast 0.9

New Functionality

  • Added Feast Job Service for management of ingestion and retrieval jobs

Note: Please see discussion thread above for functionality that did not make this release.

Feast 0.8

New Functionality

  1. Add support for AWS (data sources and deployment)

  2. Add support for local deployment

  3. Add support for Spark based ingestion

  4. Add support for Spark based historical retrieval

Technical debt, refactoring, or housekeeping

  1. Move job management functionality to SDK

  2. Remove Apache Beam based ingestion

  3. Allow direct ingestion from batch sources that does not pass through stream

  4. Remove Feast Historical Serving abstraction to allow direct access from Feast SDK to data sources for retrieval

Feast 0.7

New Functionality

Technical debt, refactoring, or housekeeping

Feast 0.6

New functionality

  1. Improved searching and filtering of features and entities

Technical debt, refactoring, or housekeeping

Feast 0.5

New functionality

Technical debt, refactoring, or housekeeping

Moved Feast Java components to

Moved Feast Spark components to

Added support for as Spark job launcher

Added Azure deployment and storage support ()

Label based Ingestion Job selector for Job Controller

Authentication Support for Java & Go SDKs

Automatically Restart Ingestion Jobs on Upgrade

Structured Audit Logging

Request Response Logging support via Fluentd

Feast Core Rest Endpoints

Improved integration testing framework

Rectify all flaky batch tests ,

Decouple job management from Feast Core

Batch statistics and validation

Authentication and authorization

Online feature and entity status metadata

Python support for labels

Improved job life cycle management

Compute and write metrics for rows prior to store writes

Streaming statistics and validation (M1 from )

Support for Redis Clusters (, )

Add feature and feature set labels, i.e. key/value registry metadata ()

Job management API ()

Clean up and document all configuration options ()

Externalize storage interfaces ()

Reduce memory usage in Redis ()

Support for handling out of order ingestion ()

Remove feature versions and enable automatic data migration () ()

Tracking of batch ingestion by with dataset_id/job_id ()

Write Beam metrics after ingestion to store (not prior) ()

Roadmap discussion
feast-java
feast-spark
Discussion
Spark on K8s Operator
#1241
Discussion
Feast 0.8 RFC
Discussion
GitHub Milestone
#903
#971
#949
#891
#961
#878
#886
#953
#982
#951
Discussion
GitHub Milestone
#612
#554
#658
#663
#761
#763
Discussion
Feature Validation RFC
#478
#502
#463
#302
#525
#402
#515
#273
#386
#462
#461
#489