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Feast

Overview

Feast is a machine learning platform. Learn more in the official Feast documentation.

The DataHub integration for Feast covers ML entities such as models, features, and related lineage metadata. Depending on module capabilities, it can also capture features such as lineage, usage, profiling, ownership, tags, and stateful deletion detection.

Concept Mapping

  • 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

Use this mapping to align feature-store metadata with existing ML entity governance patterns in DataHub.

Module feast

Certified

Important Capabilities

CapabilityStatusNotes
DescriptionsEnabled by default.
Detect Deleted EntitiesEnabled by default via stateful ingestion.
Schema MetadataEnabled by default.
Table-Level LineageEnabled by default.

Overview

The feast module ingests metadata from Feast into DataHub. It is intended for production ingestion workflows and module-specific capabilities are documented below.

Prerequisites

Before running ingestion, ensure network connectivity to the source, valid authentication credentials, and read permissions for metadata APIs required by this module.

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

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

FieldDescription
path 
string
Path to Feast repository
enable_owner_extraction
boolean
If this is disabled, then we NEVER try to map owners. If this is enabled, then owner_mappings is REQUIRED to extract ownership.
Default: False
enable_tag_extraction
boolean
If this is disabled, then we NEVER try to extract tags.
Default: False
environment
string
Environment to use when constructing URNs
Default: PROD
fs_yaml_file
One of string(path), null
Path to the feature_store.yaml file used to configure the feature store
Default: None
owner_mappings
One of array, null
Mapping of owner names to owner types
Default: None
owner_mappings.map
map(str,string)
stateful_ingestion
One of StatefulIngestionConfig, null
Stateful Ingestion Config
Default: None
stateful_ingestion.enabled
boolean
Whether or not to enable stateful ingest. Default: True if a pipeline_name is set and either a datahub-rest sink or datahub_api is specified, otherwise False
Default: False

Capabilities

Use the Important Capabilities table above as the source of truth for supported features and whether additional configuration is required.

Limitations

Module behavior is constrained by source APIs, permissions, and metadata exposed by the platform. Refer to capability notes for unsupported or conditional features.

Troubleshooting

If ingestion fails, validate credentials, permissions, connectivity, and scope filters first. Then review ingestion logs for source-specific errors and adjust configuration accordingly.

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.

💡 Contributing to this documentation

This page is auto-generated from the underlying source code. To make changes, please edit the relevant source files in the metadata-ingestion directory.

Tip: For quick typo fixes or documentation updates, you can click the ✏️ Edit icon directly in the GitHub UI to open a Pull Request. For larger changes and PR naming conventions, please refer to our Contributing Guide.