Vendor Name | Feast Feature Store |
History | Co-created by GO-JEK and Google Cloud, now governed by the Linux Foundation with Tecton as main contributor |
Stand-alone vs. Platform | Stand-alone feature store, integrates with 3rd party MLOps platforms |
Delivery Model | Open source |
Clouds Supported | AWS, GCP, Azure, On-Prem |
Pricing Model | N/A (open source only) |
Service Level Guarantees | None |
Support | N/A (open source only) |
Feature Definitions | Not available |
Automated Transforms | Not available (pipelines run outside of Feast) |
Feature Ingestion | Python (Feast 0.10) / Spark (Feast 0.9) batch feature ingestion into online store |
Storage and Feature Processing Infrastructure | Online storage: Cloud Firestore (Feast 0.10) and Redis (Feast 0.9) Offline storage: S3, Google Storage, Data Warehouse |
Feature Sharing and Discovery | Searchable feature catalog with metadata Feature discovery including feature values Feature versioning |
Training Dataset Generation | Dataset generated from offline storage using Python SDK |
Online Serving | Serving endpoint / API for online data |
Monitoring and Alerting | Not available |
Security and Data Governance | Data remains in end-user's cloud account SSO Data encryption at rest |
Integrations | Batch data: BigQuery, Google Storage, S3 |
Feast is a Python library + optional CLI. You can install it using pip.
You might want to periodically run certain Feast commands (e.g. `feast materialize-incremental
`, which updates the online store.) We recommend using schedulers such as Airflow or Cloud Composer for this.
For more details, please see the quickstart guide
No. Feast is a tool that manages data stored in other systems (e.g. BigQuery, Cloud Firestore, Redshift, DynamoDB). It is not a database, but it helps manage data stored in other systems.
Feast is available today natively on GCP/AWS, and can easily extend to work in other clouds.
No. Even though some feature stores include transformations, Feast purely manages retrieval. Feast is used alongside a separate system that computes feature values. Most often, these are pipelines written in SQL or a Python Dataframe library and scheduled to run periodically.
Gojek, Shopify, Salesforce, Twitter, Postmates, Robinhood, Porch, and Zulily are some examples of teams that are currently using the Feast Feature Store
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Vendor |
Demo link |
History |
Stand-alone vs. Platform |
Delivery Model |
Clouds Supported |
Pricing Model |
Service Level Guarantees |
Support |
Feature Definitions |
Automated Transforms |
Feature Ingestion |
Storage and Feature Processing Infrastructure |
Feature Sharing and Discovery |
Training Dataset Generation |
Online Serving |
Monitoring and Alerting |
Security and Data Governance |
Integrations |