Roadmap
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Add On-demand transformations support
Add Data quality monitoring
Add Snowflake offline store support
Add Bigtable support
Add Push/Ingestion API support
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
Full local mode support (Sqlite and Parquet)
Provider model for added extensibility
Firestore support
Native (No-Spark) BigQuery support
Added support for object store based registry
Add support for FeatureViews
Added support for infrastructure configuration through apply
Remove dependency on Feast Core
Feast Serving made optional
Moved Python API documentation to Read The Docs
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.
Add support for AWS (data sources and deployment)
Add support for local deployment
Add support for Spark based ingestion
Add support for Spark based historical retrieval
Move job management functionality to SDK
Remove Apache Beam based ingestion
Allow direct ingestion from batch sources that does not pass through stream
Remove Feast Historical Serving abstraction to allow direct access from Feast SDK to data sources for retrieval
Improved searching and filtering of features and entities
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) ()