In module 1, we focus on building features for online serving, and keeping them fresh with a combination of batch feature materialization and stream feature ingestion.

• Data sources: Kafka + File source
• Online store: Redis
• Use case: Predicting churn for drivers in real time.

First, we install Feast with Redis support:

pip install "feast[redis]"

We have already set up a feature repository in feature_repo/.

We then use Docker Compose to spin up a local Kafka cluster and automatically publish events to it.

• This leverages a script (in kafka_demo/) that creates a topic, reads from feature_repo/data/driver_stats.parquet, generates newer timestamps, and emits them to the topic.

docker-compose up

Now run the Jupyter notebook (feature_repo/workshop.ipynb)