Links & Resources

• GitHub Repository: Find the complete Feast codebase on GitHub.

  • Community Governance Doc: See the governance model of Feast, including who the maintainers are and how decisions are made.

• Google Folder: This folder is used as a central repository for all Feast resources. For example:

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

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

• Feast Linux Foundation Wiki: Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

• GitHub Issues: Found a bug or need a feature? Create an issue on GitHub.

Feast project structure

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

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).

Data ingestion

For offline use cases that only rely on batch data, Feast does not need to ingest data and can query your existing data (leveraging a compute engine, whether it be a data warehouse or (experimental) Spark / Trino). Feast can help manage pushing streaming features to a batch source to make features available for training.

Feature registration and retrieval

Features are registered as code in a version controlled repository, and tie to data sources + model versions via the concepts of entities, feature views, and feature services. We explore these concepts more in the upcoming concept pages. These features are then stored in a registry, which can be accessed across users and services. The features can then be retrieved via SDK API methods or via a deployed feature server which exposes endpoints to query for online features (to power real time models).

Feast supports several patterns of feature retrieval.

Data source

A data source in Feast refers to raw underlying data that users own (e.g. in a table in BigQuery). Feast does not manage any of the raw underlying data but instead, is in charge of loading this data and performing different operations on the data to retrieve or serve features.

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 materialize features into an online store.

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

Feast supports primarily time-stamped tabular data as data sources. There are many kinds of possible data sources:

• Batch data sources: ideally, these live in data warehouses (BigQuery, Snowflake, Redshift), but can be in data lakes (S3, GCS, etc). Feast supports ingesting and querying data across both.

• Stream data sources: Feast does not have native streaming integrations. It does however facilitate making streaming features available in different environments. There are two kinds of sources:

  • Push sources allow users to push features into Feast, and make it available for training / batch scoring ("offline"), for realtime feature serving ("online") or both.

  • [Alpha] Stream sources allow users to register metadata from Kafka or Kinesis sources. The onus is on the user to ingest from these sources, though Feast provides some limited helper methods to ingest directly from Kafka / Kinesis topics.

Batch data ingestion

Ingesting from batch sources is only necessary to power real-time models. This is done through materialization. Under the hood, Feast manages an offline store (to scalably generate training data from batch sources) and an online store (to provide low-latency access to features for real-time models).

A key command to use in Feast is the materialize_incremental command, which fetches the latest values for all entities in the batch source and ingests these values into the online store.

Materialization can be called programmatically or through the CLI:

Batch data schema inference

If the schema parameter is not specified when defining a data source, Feast attempts to infer the schema of the data source during feast apply. The way it does this depends on the implementation of the offline store. For the offline stores that ship with Feast out of the box this inference is performed by inspecting the schema of the table in the cloud data warehouse, or if a query is provided to the source, by running the query with a LIMIT clause and inspecting the result.

Stream data ingestion

Ingesting from stream sources happens either via a Push API or via a contrib processor that leverages an existing Spark context.