Demo Notebooks
Feast can generate tailored Jupyter notebooks for any Feast project. The notebooks adapt to your feature_store.yaml configuration and provide a hands-on walkthrough of core Feast functionality.
What you get
For each project discovered, Feast creates a directory with notebooks covering:
01 — Feature Store Overview
Explore registered entities, feature views, feature services, and data sources.
02 — Historical Feature Retrieval
Build a training dataset with point-in-time correct joins using get_historical_features.
03 — Online Feature Serving
Materialize features to the online store and retrieve them at low latency with get_online_features.
The content adapts automatically based on:
Online / offline store types — descriptions reflect the actual backends configured.
Registry type — local registries include
feast apply; remote registries userefresh_registry().Authentication — auth details from
feature_store.yamlare surfaced when configured.Vector search — a vector/RAG retrieval section is included when embeddings are detected.
Prerequisites
Python 3.9+
Feast installed (
pip install feast)A feature repository with a valid
feature_store.yaml
Using the CLI
Run the command from (or pointing to) a directory containing feature_store.yaml:
This searches for feature_store.yaml in the current directory and every file inside the feast-config/ directory. Each file in feast-config/ is treated as a separate project config. For each project found, notebooks are written to ./feast-demo-notebooks/<project>/.
Options
-o, --output-dir
./feast-demo-notebooks
Root directory for generated notebooks
--overwrite
false
Overwrite if the output directory already exists
Using the Python SDK
Parameters
output_dir
str
"./feast-demo-notebooks"
Root directory for generated notebooks
repo_path
str
"."
Directory to search for feature_store.yaml files
overwrite
bool
False
Overwrite existing output directories
Examples
Multi-project repositories
If your feast-config/ directory contains multiple files, each is treated as a separate project and a dedicated notebook directory is created:
Running the notebooks
Open any generated notebook in Jupyter, JupyterLab, or VS Code and run cells from top to bottom. Each notebook:
Configures the path to your
feature_store.yamlautomatically (no manual editing needed).Connects to the feature store using the Feast Python SDK.
Walks through relevant operations with real data from your project.
The first notebook (01 — Overview) includes a prerequisites check and feast apply / registry sync step. Subsequent notebooks assume these have already been completed.
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