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.
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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