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Feature Store Bootstrap
Feature store patterns using Feast, feature engineering pipelines, and offline/online serving configurations.
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📁 File Structure 8 files
feature-store-bootstrap/
├── LICENSE
├── README.md
├── config.example.yaml
├── docs/
│ ├── checklists/
│ │ └── pre-deployment.md
│ ├── overview.md
│ └── patterns/
│ └── pattern-01-offline-online-sync.md
└── templates/
└── config.yaml
📖 Documentation Preview README excerpt
Feature Store Bootstrap
Production-ready feature store setup using Feast with feature engineering pipelines and offline/online serving patterns. Go from ad-hoc feature computation to a centralized, reusable feature platform.
What's Included
- Feast feature store configuration and deployment
- Feature engineering pipeline templates
- Offline feature serving for training
- Online feature serving for real-time inference
- Feature validation and monitoring
- Point-in-time correct feature retrieval patterns
- Entity and feature view definitions
Quick Start
# 1. Copy the example config
cp config.example.yaml config.yaml
# 2. Initialize the Feast feature repository
feast init my_feature_repo
# 3. Apply feature definitions
feast apply
# 4. Materialize features to online store
feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S")
Prerequisites
- Python 3.9+
- Feast 0.30+
- Redis (for online store) or DynamoDB/Bigtable
- Data warehouse access (BigQuery, Redshift, or Snowflake)
Contents
feature-store-bootstrap/
config.example.yaml
docs/
overview.md
patterns/
pattern-01-*.md
checklists/
pre-deployment.md
templates/
config.yaml
Support
For questions or issues, contact: megafolder122122@hotmail.com
License
MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.
📄 Code Sample .yaml preview
config.example.yaml
# Feature Store Bootstrap - Example Configuration
# Copy this file to config.yaml and update values for your environment
feast:
project: "my_ml_project"
registry: "./feature_repo/registry.db"
provider: "local"
# For production: "gcp", "aws", or "azure"
offline_store:
type: "file"
# For BigQuery: type: "bigquery", project: "your-gcp-project"
# For Redshift: type: "redshift", cluster_id: "your-cluster"
online_store:
type: "sqlite"
path: "./feature_repo/online_store.db"
# For Redis: type: "redis", connection_string: "redis://localhost:6379"
# For DynamoDB: type: "dynamodb", region: "us-east-1"
materialization:
interval_minutes: 60
incremental: true
feature_engineering:
pipelines:
- name: "user_features"
source: "users_table"
schedule: "0 * * * *" # Hourly
- name: "transaction_features"
source: "transactions_table"
schedule: "*/15 * * * *" # Every 15 minutes
logging:
level: "INFO"