Recipe and nutrition data pipeline for meal planning

A data pipeline for managing recipes and nutritional information across weekly meal plans. Fetches from official nutrition databases, processes, and stores in Airtable for meal planning workflows.

Problem

Creating accurate meal plans requires reliable nutritional data. That data lives in government databases (USDA, Turkish TürKomp) with different formats and APIs. You need a pipeline to fetch, normalize, and make it usable for recipe management.

Architecture

Script-based pipeline with Airtable as the central database:

┌─────────────────┐     ┌─────────────────┐
│  USDA FoodData  │     │    TürKomp      │
│    Central      │     │  (Turkish DB)   │
└────────┬────────┘     └────────┬────────┘
         │                       │
         └───────────┬───────────┘
                     ▼
         ┌─────────────────────┐
         │   Python Scripts    │
         │  (fetch, process)   │
         └──────────┬──────────┘
                    ▼
    ┌───────────────────────────────┐
    │         Airtable              │
    │  ┌─────────┐  ┌─────────┐    │
    │  │ Base 1  │──│ Base 2  │    │
    │  │(recipes)│  │(planning)│   │
    │  └─────────┘  └─────────┘    │
    └───────────────────────────────┘

Cross-Base Record Linking: Base 1 is source of truth for recipes. Base 2 links to it for meal planning. No duplication—changes propagate automatically.

Data Sources

Source	Content
USDA FoodData Central	US nutritional data, standard reference
TürKomp	Turkish food composition database
Manual entries	Custom recipes with calculated nutrition

Current Status

Why This Exists

Meal planning at scale needs data infrastructure. This project treats recipes as data engineering problem—fetch from authoritative sources, normalize formats, maintain single source of truth. Airtable provides the flexibility of a spreadsheet with the structure of a database.