Feathr
- Android app
- Not listed
- Free plan
- No
- Runs on
- api, Linux, self-hosted, Web
Summary
Feathr is an open-source platform for data and AI engineering, built to define, register, and share data and feature transformations. Its Python client offers APIs and customizable user-defined functions with native PySpark and Spark SQL support. The transformation API works across offline batch, streaming, and online environments. Point-in-time-correct joins connect computed features to training data to help avoid leakage; supported techniques include time-based aggregations, sliding-window joins, lookup features, and derived features. An optional UI and registry let teams search features, review metadata and lineage, manage access, and share features. Registry backends include Azure Purview and ANSI SQL, and deployment exposes a REST API. Integrations listed include Azure Synapse, Databricks, Azure Machine Learning, Jupyter Notebook, Azure Blob Storage, AWS S3, Snowflake, Kafka, Redis, and Azure Cosmos DB. Feathr documents Azure deployment and offers a Docker sandbox and locally installable Python client. It is available under the Apache License 2.0. Its FAQ notes that preprocessing currently seems to accept only one UDF function.
Who it is for
Feathr suits teams building feature transformations for machine-learning workflows across batch, streaming, or online settings. Its registry, access controls, and listed cloud and data integrations are relevant to teams sharing features.
What is good
- Supports offline batch, streaming, and online processing.
- Point-in-time joins help avoid data leakage.
- Registry supports searching, lineage, and access management.
- Provides Python APIs with PySpark and Spark SQL support.
- Available under the Apache License 2.0.
What to know first
- Preprocessing currently seems to accept only one UDF function.
- Feature-level access control is not supported yet.
- Role-based access control requires a SQL service.
- Feature monitoring is not supported.
Verdict
Feathr brings transformation, point-in-time joins, and a shareable feature registry into one platform, with documented integrations and deployment options. Its preprocessing and access-control limitations merit review against a team's requirements.
Compared on feature store software
- Online store
- Yesgithub.com
- Offline store
- Yesgithub.com
- Point-in-time joins
- Yesgithub.com
- Feature monitoring
- Nogithub.com
- Deployment model
- bothgithub.com
- Serving modes
- bothgithub.com
Facts
- Purpose
- Feathr is a data and AI engineering platform for defining, registering, and sharing data and feature transformations.github.com · 3 Oct 2026
- AI modeling
- Feathr computes feature transformations and joins them to training data using point-in-time-correct semantics to help avoid data leakage.github.com · 3 Oct 2026
- Transformation API
- Feathr provides Pythonic APIs and customizable user-defined functions with native PySpark and Spark SQL support.github.com · 3 Oct 2026
- Processing modes
- Its unified transformation API supports offline batch, streaming, and online environments.github.com · 3 Oct 2026
- Scale
- The project says Feathr can process billions of rows and petabyte-scale data using optimizations such as bloom filters and salted joins.github.com · 3 Oct 2026
- Feature registry
- The built-in registry supports searching features, viewing data sources and lineage, and managing access controls.github.com · 3 Oct 2026
- Cloud and compute integrations
- Documented integrations include Azure Synapse, Databricks, Azure Machine Learning, and Jupyter Notebook.github.com · 3 Oct 2026
- Storage and streaming integrations
- Documented options include Azure Blob Storage, Azure ADLS Gen2, AWS S3, Snowflake, Kafka, EventHub, Redis, and Azure Cosmos DB.github.com · 3 Oct 2026
- Security controls
- The registry access control documentation describes project-level role-based access control with admin, producer, and consumer roles, and says feature-level access control is not supported yet.feathr-ai.github.io · 3 Oct 2026
- Secret handling
- The configuration guide says settings can be stored in Kubernetes secrets or a key vault, and that Azure Key Vault is currently supported for retrieving values.feathr-ai.github.io · 3 Oct 2026
- Installation and deployment
- The project documents installing its Python client with pip and deploying Feathr on Azure, Databricks, or Azure Synapse; its sandbox is distributed as a Docker container.github.com · 3 Oct 2026
- License and availability
- The GitHub repository is public and its license file specifies Apache License 2.0.github.com · 3 Oct 2026
- Community support
- The project directs users to its Slack channel and GitHub Discussions for questions and discussion.github.com · 3 Oct 2026
- Project stewardship
- The repository says Feathr is a project under the LF AI & Data Foundation and was open sourced in 2022.github.com · 3 Oct 2026
- Feature engineering
- It supports time based aggregations, sliding window joins, lookup features, and derived features with point in time correctness.github.com · 4 Oct 2026
- Interfaces
- Feathr provides Pythonic APIs and customizable user defined functions with native PySpark and Spark SQL support.github.com · 4 Oct 2026
- Execution modes
- Its unified data transformation API works in offline batch, streaming, and online environments.github.com · 4 Oct 2026
- Registry and governance
- The optional UI and registry let users search features, inspect metadata and lineage, manage access controls, and share features across teams.feathr-ai.github.io · 4 Oct 2026
- Registry backends
- The feature registry supports Azure Purview and ANSI SQL backends; role based access control requires a SQL service to store its related information.feathr-ai.github.io · 4 Oct 2026
- Integrations
- Listed integrations include Azure Blob Storage, ADLS Gen2, AWS S3, Azure SQL, Snowflake, Kafka, EventHub, Redis, Azure Cosmos DB, Databricks, and Azure Synapse.github.com · 4 Oct 2026
- ML tools
- The project lists Azure Machine Learning, Jupyter Notebook, and Databricks Notebook as machine learning platform integrations.github.com · 4 Oct 2026
- Deployment
- The project documents Azure deployment and provides a self contained Docker sandbox, plus a locally installable Python client.github.com · 4 Oct 2026
- API
- The registry deployment exposes a REST API, and both the Feathr UI and Python client interact with it.feathr-ai.github.io · 4 Oct 2026
- Support
- The project directs users to its Slack channel for questions and discussions.github.com · 4 Oct 2026
- Notable limit
- The project FAQ says preprocessing currently seems to accept only one UDF function, subject to change based on requirements.feathr-ai.github.io · 4 Oct 2026
- Intended use
- The FAQ says a feature store is typically useful when modeling entities such as users, accounts, or items, and may not be necessary for regular image recognition.feathr-ai.github.io · 4 Oct 2026
- Project status
- The README says Feathr was open sourced in 2022 and is a project under the LF AI & Data Foundation.github.com · 4 Oct 2026
Company
- Founded
- 2017github.com · 28 Sept 2026
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Sources
- github.com/feathr-ai/feathr· checked 3 Oct 2026
- feathr-ai.github.io/feathr/concepts/registry-access-control· checked 3 Oct 2026
- feathr-ai.github.io/feathr/how-to-guides/feathr-configurati· checked 3 Oct 2026
- github.com/feathr-ai/feathr/blob/main/LICENSE· checked 3 Oct 2026
- feathr-ai.github.io/feathr/concepts/feature-registry.html· checked 4 Oct 2026
- feathr-ai.github.io/feathr/concepts/faq.html· checked 4 Oct 2026