Build a Feature Store for Machine Learning Using Python & Redis End-to-End Data Engineering Project

This project focuses on building a Feature Store, a core system used by companies like: Uber Airbnb DoorDash Netflix A feature store solves a major ML problem: 👉 Training-serving skew Without a feature store: Data used in training ≠ data used in production Models fail in real-world scenarios A feature store ensures: Consistent features Reusable pipelines Real-time + batch access This is one of the most important modern data engineering concepts. 🧰 TOOLS & TECHNOLOGIES USED Programming Python 3.10+ Pandas Storage PostgreSQL (offline store) Redis (online store) Processing Batch jobs (Python) Streaming (optional) APIs FastAPI Utilities Docker Git & GitHub 📁 PROJECT FOLDER STRUCTURE feature_store_project/ │ ├── ingestion/ │ └── load_data.py │ ├── features/ │ └── feature_engineering.py │ ├── offline_store/ │ └── postgres_store.py │ ├── online_store/ │ └── redis_store.py │ ├── serving/ │ └── api.py │ ├── training/ │ └── train_model.py │ ├── requirements.txt └── README.md 📂 DATA REQUIRED Use user behavior or transaction data: user_id timestamp transaction_amount num_transactions avg_transaction_value location device Goal: convert raw data into ML-ready features. 🧠 STEP-BY-STEP IMPLEMENTATION 🔹 STEP 1: Data Ingestion import pandas as pd df = pd.read_csv("transactions.csv") This simulates: Batch ingestion from logs Data lake pipelines 🔹 STEP 2: Feature Engineering df['avg_txn'] = df['transaction_amount'] / df['num_transactions'] df['txn_velocity'] = df.groupby('user_id')['transaction_amount'].transform('count') Create features such as: user spending behavior transaction frequency rolling averages time-based features 🔹 STEP 3: Store Features in Offline Store from sqlalchemy import create_engine engine = create_engine("postgresql://user:pass@localhost/db") df.to_sql("features_offline", engine, if_exists="replace") Offline store is used for: model training historical analysis 🔹 STEP 4: Store Features in Onl