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.
If you need a managed feature store that provides feature computation, check out Tecton.
Is Feast a database?
No. Feast is a tool that manages data stored in other systems, (e.g. BigQuery or Cloud Firestore.) It is not a database, but it helps manage data stored in other systems.
How do I install and run Feast?
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.
Feast also supports optional deployment configurations that target Kubernetes. This deployment model works wherever Kubernetes can be run, like on-prem, and includes a job scheduler.
What clouds does Feast work on?
Feast is available today natively on GCP, and you can run Feast on Kubernetes on AWS. The next release of Feast aims to bring Feast to AWS.
Additionally, Feast supports optional deployment configurations that target Kubernetes. This deployment model works wherever Kubernetes can be run, like on-prem.
What data stores does Feast support?
Feast supports Google BigQuery, and Google Cloud Firestore as an online store. The next release of Feast aims to add support for Google BigTable as an online store.
Do I have to use Kubernetes, Spark, or Terraform?
Not by default. Feast does support optional deployment configurations that include Kubernetes, Spark, and Terraform. See Feast on Kubernetes for more details.
Does Feast support streaming data?
Not by default. Optional components that are deployed on Kubernetes can handle the ingestion of streaming data. See Feast on Kubernetes for more details.