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

• Items below that are in development (or planned for development) will be indicated in parentheses.

• We welcome contribution to all items in the roadmap!

• Want to influence our roadmap and prioritization? Submit your feedback to this form.

• Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using Calendly.

• Data Sources

  • Snowflake source

  • Redshift source

  • BigQuery source

  • Parquet file source

  • Synapse source (community plugin)

  • Hive (community plugin)

  • Postgres (community plugin)

  • Spark (community plugin)

  • Kafka source (with push support into the online store)

  • HTTP source

• Offline Stores

  • Snowflake

  • Redshift

  • BigQuery

  • Synapse (community plugin)

  • Hive (community plugin)

  • Postgres (community plugin)

  • Trino (community plugin)

  • Spark (community plugin)

  • In-memory / Pandas

  • Custom offline store support

• Online Stores

  • DynamoDB

  • Redis

  • Datastore

  • SQLite

  • Azure Cache for Redis (community plugin)

  • Postgres (community plugin)

  • Custom online store support

  • Bigtable

  • Cassandra

• Streaming

  • Custom streaming ingestion job support

  • Push based streaming data ingestion

  • Streaming ingestion on AWS

  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Alpha release. See RFC)

  • Batch transformation (SQL. In progress. See RFC)

  • Streaming transformation

• Deployments

  • AWS Lambda (Alpha release. See RFC)

  • Kubernetes (See guide)

  • Cloud Run

  • KNative

• Feature Serving

  • Python Client

  • REST Feature Server (Python) (Alpha release. See RFC)

  • gRPC Feature Server (Java) (See #1497)

  • Push API

  • Java Client

  • Go Client

  • Delete API

  • Feature Logging (for training)

• Data Quality Management (See RFC)

  • Data profiling and validation (Great Expectations)

  • Metric production

  • Training-serving skew detection

  • Drift detection

• 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)

  • REST API for browsing feature registry

  • Feast Web UI

  • Feature versioning

The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.

Project

Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev, staging, prod).

The data source refers to raw underlying data (e.g. a table in BigQuery).

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or when materializing features into an online store.

Below is an example data source with a single entity (driver) and two features (trips_today, and rating).

Links & Resources

• • Design proposals in the form of Request for Comments (RFC).

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

How can I get help?

• Slack: Need to speak to a human? Come ask a question in our Slack channel (link above).

Community Calls

We have a user and contributor community call every two weeks (Asia & US friendly).

Frequency (alternating times every 2 weeks)

• Tuesday 18:00 pm to 18:30 pm (US, Asia)

• Tuesday 10:00 am to 10:30 am (US, Europe)

Links