Last updated
Was this helpful?
The is the easiest way to learn about Feast. For more detailed tutorials, please check out the page.
Feature tables from Feast 0.9 have been renamed to feature views in Feast 0.10+. For more details, please see the discussion .
No, there are .
Feast currently does not support any access control other than the access control required for the Provider's environment (for example, GCP and AWS permissions).
Feast is actively working on this right now. Please reach out to the Feast team if you're interested in giving feedback!
A feature view can be defined with multiple entities. Since each entity has a unique join_key, using multiple entities will achieve the effect of a composite key.
Feast is designed to work at scale and support low latency online serving. Benchmarks to be released soon, and active work is underway to support very latency sensitive use cases.
Yes. Specifically:
Simple lists / dense embeddings:
BigQuery supports list types natively
Redshift does not support list types, so you'll need to serialize these features into strings (e.g. json or protocol buffers)
Sparse embeddings (e.g. one hot encodings)
Yes. There are two ways to use S3 in Feast:
Using the s3_endpoint_override in a FileSource data source. This endpoint is more suitable for quick proof of concepts that won't necessarily scale for production use cases.
Please see a detailed comparison of Feast vs. Tecton . For another comparison, please see .
Feast's implementation of online stores serializes features into Feast protocol buffers and supports list types (see )
One way to do this efficiently is to have a protobuf or string representation of
The list of supported offline and online stores can be found and , respectively. The indicates the stores for which we are planning to add support. Finally, our Provider abstraction is built to be extensible, so you can plug in your own implementations of offline and online stores. Please see more details about custom providers .
Please follow the instructions .
Using Redshift as a data source via Spectrum (), and then continuing with the guide. See a we did on this at our apply() meetup.
Feast does not support Spark natively. However, you can create a that will support Spark, which can help with more scalable materialization and ingestion.
Please see the .
Feast 0.10+ is much lighter weight and more extensible than Feast 0.9. It is designed to be simple to install and use. Please see this for more details.
Please see this . If you have any questions or suggestions, feel free to leave a comment on the document!
For more details on contributing to the Feast community, see and this .
Feast Core and Feast Serving were both part of Feast Java. We plan to support Feast Serving. We will not support Feast Core; instead we will support our object store based registry. We will not support Feast Spark. For more details on what we plan on supporting, please see the .
We encourage you to ask questions on or . Even better, once you get an answer, add the answer to this FAQ via a !