Feast + Denormalized

This repository contains an example application of using Denormalized to process features in real-time and sink them to an online feast store.

Install UV

• uv venv --python 3.12 && source .venv/bin/activate

• uv sync --dev

• uv pip install -e .

• Start kafka in docker docker run -p 9092:9092 --name kafka apache/kafka

• create the feature store: python src/feature_repo/

• Start emitting events: python src/session_generator/

• Start the pipelines: python src/pipelines/

It is also possible to run the example using the provider docker-compose file:

• docker compose up --build

The features can be viewed in realtime using the print_features.ipynb notebook jupyter-lab