Feast

Important Capabilities

Capability	Status	Notes
Descriptions	✅	Enabled by default
Schema Metadata	✅	Enabled by default
Table-Level Lineage	✅	Enabled by default

This plugin extracts:

• Entities as MLPrimaryKey
• Fields as MLFeature
• Feature views and on-demand feature views as MLFeatureTable
• Batch and stream source details as Dataset
• Column types associated with each entity and feature

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[feast]'

Starter Recipe

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: feast
  config:
    # Coordinates
    path: "/path/to/repository/"
    # Options
    environment: "PROD"

sink:
  # sink configs

Config Details

Options

Note that a . is used to denote nested fields in the YAML recipe.

Field	Description
path ✅ string	Path to Feast repository
environment string	Environment to use when constructing URNs Default: PROD
fs_yaml_file string	Path to the feature_store.yaml file used to configure the feature store

Schema

The JSONSchema for this configuration is inlined below.

{
  "title": "FeastRepositorySourceConfig",
  "type": "object",
  "properties": {
    "path": {
      "title": "Path",
      "description": "Path to Feast repository",
      "type": "string"
    },
    "fs_yaml_file": {
      "title": "Fs Yaml File",
      "description": "Path to the `feature_store.yaml` file used to configure the feature store",
      "type": "string"
    },
    "environment": {
      "title": "Environment",
      "description": "Environment to use when constructing URNs",
      "default": "PROD",
      "type": "string"
    }
  },
  "required": [
    "path"
  ],
  "additionalProperties": false
}

Code Coordinates

• Class Name: datahub.ingestion.source.feast.FeastRepositorySource
• Browse on GitHub

Questions

If you've got any questions on configuring ingestion for Feast, feel free to ping us on our Slack.