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{
  "filters": {
    "countries": ["US"]
  },
  "datasets": ["states"],
  "fields": ["name", "state_code", "latitude", "longitude"]
}
// Result: Only US states (50 records instead of 4,000+)
// Cost: Still 2 credits for states dataset

{
  "countries": [
    {
      "name": "United States",
      "iso2": "US",
      "states_count": 50,
      "cities_count": 19495
    }
  ],
  "states": [
    {
      "name": "California", 
      "country_code": "US",
      "cities_count": 1538
    }
  ],
  "cities": [
    {
      "name": "Los Angeles",
      "state_name": "California",
      "country_name": "United States"
    }
  ]
}

# countries.csv
name,iso2,states_count,cities_count
United States,US,50,19495

# states.csv  
name,country_code,country_name,cities_count
California,US,United States,1538

# cities.csv
name,state_name,country_name,population
Los Angeles,California,United States,3971883

// Load and cache data
const geoData = await fetch('/data/export.json').then(r => r.json());

// Create lookup maps for performance
const countryMap = new Map(geoData.countries.map(c => [c.iso2, c]));
const stateMap = new Map(geoData.states.map(s => [s.state_code, s]));

// Use in components
function CountryDropdown() {
  return (
    <select>
      {geoData.countries.map(country => (
        <option key={country.iso2} value={country.iso2}>
          {country.emoji} {country.name}
        </option>
      ))}
    </select>
  );
}

# PostgreSQL
psql -d mydb -f csc_export.sql

# MySQL
mysql -u user -p mydb < csc_export.sql

# SQLite
sqlite3 mydb.db < csc_export.sql

import pandas as pd

# Load data
countries = pd.read_csv('countries.csv')
states = pd.read_csv('states.csv')

# Analysis
regional_stats = countries.groupby('region').agg({
    'population': 'sum',
    'gdp': 'mean'
})

apiVersion: v1
kind: ConfigMap
metadata:
  name: geo-config
data:
  countries.yaml: |
    # CSC Export Data
    countries:
      - name: "United States"
        currency: "USD"