Roadmap

The list below contains the functionality that contributors are planning to develop for Feast.

• We welcome contribution to all items in the roadmap!

• Natural Language Processing

  • Vector Search (Alpha release. See RFC)

  • Enhanced Feature Server and SDK for native support for NLP

• Data Sources

  • Snowflake source

  • Redshift source

  • BigQuery source

  • Parquet file source

  • Azure Synapse + Azure SQL source (contrib plugin)

  • Hive (community plugin)

  • Postgres (contrib plugin)

  • Spark (contrib plugin)

  • Couchbase (contrib plugin)

  • Athena (contrib plugin)

  • Clickhouse (contrib plugin)

  • Oracle (contrib plugin)

  • Kafka / Kinesis sources (via push support into the online store)

• Offline Stores

  • Snowflake

  • Redshift

  • BigQuery

  • DuckDB

  • Dask

  • Remote

  • Azure Synapse + Azure SQL (contrib plugin)

  • Hive (community plugin)

  • Postgres (contrib plugin)

  • Trino (contrib plugin)

  • Spark (contrib plugin)

  • Couchbase (contrib plugin)

  • Athena (contrib plugin)

  • Clickhouse (contrib plugin)

  • Ray (contrib plugin)

  • Oracle (contrib plugin)

  • Hybrid

  • Custom offline store support

• Online Stores

  • Snowflake

  • DynamoDB

  • Redis

  • Dragonfly

  • Datastore

  • Bigtable

  • SQLite

  • Remote

  • Postgres

  • HBase

  • Cassandra / AstraDB

  • ScyllaDB

  • MySQL

  • Hazelcast

  • Elasticsearch

  • SingleStore

  • Couchbase

  • MongoDB

  • Qdrant (vector store)

  • Milvus (vector store)

  • Faiss (vector store)

  • Hybrid

  • Azure Cache for Redis (community plugin)

  • Custom online store support

• Feature Engineering

  • On-demand Transformations (On Read) (Beta release. See RFC)

  • Streaming Transformations (Alpha release. See RFC)

  • Batch transformation (In progress. See RFC)

  • On-demand Transformations (On Write) (Beta release. See GitHub Issue)

• Streaming

  • Custom streaming ingestion job support

  • Push based streaming data ingestion to online store

  • Push based streaming data ingestion to offline store

• Deployments

  • AWS Lambda (Alpha release. See RFC)

  • Kubernetes (See guide)

• Feature Serving

  • Python Client

  • Python feature server

  • Feast Operator (alpha)

  • Java feature server (alpha)

  • Go feature server (alpha)

  • Offline Feature Server (alpha)

  • Registry server (alpha)

• Data Quality Management (See RFC)

  • Data profiling and validation (Great Expectations)

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • Amundsen integration (see Feast extractor)

  • DataHub integration (see DataHub Feast docs)

  • Feast Web UI (Beta release. See docs)

  • Feast Lineage Explorer