Table of contents

• Introduction
• Blog
• Community & getting help
• Roadmap
• Changelog

Getting started

• Quickstart
• GenAI
• Architecture
  • Overview
  • Language
  • Push vs Pull Model
  • Write Patterns
  • Feature Transformation
  • Feature Serving and Model Inference
  • Role-Based Access Control (RBAC)
• Concepts
  • Overview
  • Project
  • Data ingestion
  • Entity
  • Data types
  • Feature repository
  • Feature view
  • Batch feature view
  • Stream feature view
  • Tiling with Intermediate Representations
  • Label view
  • Feature retrieval
  • Point-in-time joins
  • [Alpha] Saved dataset
  • Permission
  • Tags
• Use Cases
• Components
  • Overview
  • Registry
  • Offline store
  • Online store
  • Feature server
  • Compute Engine
  • Stream Processor
  • Provider
  • Authorization Manager
  • OpenTelemetry Integration
• Third party integrations
• FAQ

Tutorials

• Sample use-case tutorials
  • Driver ranking
  • Fraud detection on GCP
  • Real-time credit scoring on AWS
  • Driver stats on Snowflake
• Validating historical features with Great Expectations
• Building streaming features
• Retrieval Augmented Generation (RAG) with Feast
• RAG Fine Tuning with Feast and Milvus
• MCP - AI Agent Example
• Feast-Powered AI Agent
• Demo Notebooks
• Feature Quality Monitoring Quickstart

How-to Guides

• Running Feast with Snowflake/GCP/AWS
  • 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
  • Scaling Feast
  • Structuring Feature Repos
  • Multi-Team Feature Store Setup
• Running Feast in production (e.g. on Kubernetes)
• Feast on Kubernetes
  • Operator Configuration Guides
    • 1 — Project Provisioning
    • 2 — Persistence
    • 3 — Serving & Observability
    • 4 — Registry Topology
    • 5 — Security
    • 6 — Batch & Jobs
    • 7 — OpenLineage & Materialization
• Feast Production Deployment Topologies
• Online Server Performance Tuning
• Customizing Feast
  • Adding a custom compute engine
  • Adding a new offline store
  • Adding a new online store
  • Adding a custom provider
• Adding or reusing tests
• Starting Feast servers in TLS(SSL) Mode
• Importing Features from dbt
• Entity Key Serialization (v2 to v3)
• Feature Quality Monitoring

Reference

• Codebase Structure
• Type System
• Data sources
  • Overview
  • Table formats
  • File
  • Snowflake
  • BigQuery
  • Redshift
  • Push
  • Kafka
  • Kinesis
  • Spark (contrib)
  • PostgreSQL (contrib)
  • Trino (contrib)
  • Azure Synapse + Azure SQL (contrib)
  • Couchbase (contrib)
  • Oracle (contrib)
  • Athena (contrib)
  • Clickhouse (contrib)
  • Ray (contrib)
  • MongoDB (contrib)
• Offline stores
  • Overview
  • Dask
  • Snowflake
  • BigQuery
  • Redshift
  • DuckDB
  • Couchbase Columnar (contrib)
  • Spark (contrib)
  • PostgreSQL (contrib)
  • Trino (contrib)
  • Azure Synapse + Azure SQL (contrib)
  • Clickhouse (contrib)
  • Ray (contrib)
  • Oracle (contrib)
  • Athena (contrib)
  • MongoDB (contrib)
  • Remote Offline
  • Hybrid
• Online stores
  • Overview
  • SQLite
  • Snowflake
  • Redis
  • Dragonfly
  • Datastore
  • DynamoDB
  • Bigtable
  • Remote
  • PostgreSQL
  • HBase
  • Cassandra + Astra DB
  • Couchbase
  • MySQL
  • Hazelcast
  • ScyllaDB
  • SingleStore
  • Milvus
  • MongoDB
  • Elasticsearch
  • Qdrant
  • Faiss
  • Hybrid
• Registries
  • Metadata
  • Local
  • S3
  • GCS
  • SQL
  • Snowflake
  • HDFS
  • Remote
  • Registry permissions
• Providers
  • Local
  • Google Cloud Platform
  • Amazon Web Services
  • Azure
• Compute Engines
  • Snowflake
  • AWS Lambda (alpha)
  • Spark (contrib)
  • Apache Flink
  • Ray (contrib)
• Feature repository
  • feature_store.yaml
  • .feastignore
  • Registration inferencing
• Kubernetes auth setup
• User token provisioning
• Feature servers
  • Python feature server
  • [Alpha] Go feature server
  • MCP Feature Server
  • Offline Feature Server
  • Registry server
• [Beta] Web UI
• [Beta] On demand feature view
• [Alpha] Static Artifacts Loading
• [Alpha] Vector Database
• [Alpha] Data quality monitoring
• [Alpha] Streaming feature computation with Denormalized
• [Alpha] Feature View Versioning
• OpenLineage Integration
• MLflow Integration
• Feast CLI reference
• Python API reference
• Usage

Project

• Contribution process
• Development guide
• Backwards Compatibility Policy
  • Maintainer Docs
• Versioning policy
• Release process
• Feast 0.9 vs Feast 0.10+
• Architecture Decision Records
  • ADR-0001: Feature Services
  • ADR-0002: Component Refactor
  • ADR-0003: On-Demand Transformations
  • ADR-0004: Entity Join Key Mapping
  • ADR-0005: Stream Transformations
  • ADR-0006: Kubernetes Operator
  • ADR-0007: Unified Feature Transformations
  • ADR-0008: Feature View Versioning
  • ADR-0009: Contribution and Extensibility
  • ADR-0010: Vector Database Integration
  • ADR-0011: Data Quality Monitoring
  • ADR-0012: LabelView