Nested JSON Flattening

Advanced strategies for converting hierarchical JSON to flat CSV

📅 Updated February 2026⏱️ 10 min read

The Flattening Challenge

One of the biggest challenges in data format conversion is handling nested JSON structures. APIs often return deeply nested objects and arrays, but CSV and Excel require flat, tabular data. This guide covers proven strategies for flattening complex JSON while preserving data integrity.

⚠️ Note: There's no perfect universal solution—the best approach depends on your data structure and use case.

Strategy 1: Dot Notation Flattening

Transform nested object paths into flat column names using dot notation.

Example

Input JSON

{
  "id": 1,
  "user": {
    "name": "Alice",
    "contact": {
      "email": "alice@ex.com",
      "phone": "555-0100"
    }
  }
}

Flattened CSV

id,user.name,user.contact.email,user.contact.phone
1,Alice,alice@ex.com,555-0100

Pros & Cons

✓ Advantages

  • • Preserves all data
  • • No data duplication
  • • Reversible (can reconstruct JSON)
  • • Clear column naming

✗ Disadvantages

  • • Long column names
  • • Doesn't handle arrays well
  • • Deep nesting creates many columns
  • • Less intuitive for non-technical users

Strategy 2: Array Expansion

Create multiple rows for array items, duplicating parent data in each row.

Example

Input JSON

{
  "orderId": "A123",
  "customer": "Alice",
  "items": [
    {"product": "Laptop", "qty": 1},
    {"product": "Mouse", "qty": 2}
  ]
}

Expanded CSV

orderId,customer,product,qty
A123,Alice,Laptop,1
A123,Alice,Mouse,2

Pros & Cons

✓ Advantages

  • • Intuitive tabular format
  • • Easy to filter/analyze per item
  • • Works well with reporting tools
  • • Natural for one-to-many relationships

✗ Disadvantages

  • • Data duplication (larger files)
  • • Harder to reconstruct original JSON
  • • Variable row count per record
  • • Nested arrays create exponential rows

Strategy 3: JSON String Preservation

Keep complex nested structures as JSON strings within CSV cells.

Example

Input JSON

{
  "id": 1,
  "name": "Alice",
  "metadata": {
    "tags": ["vip", "priority"],
    "preferences": {
      "theme": "dark",
      "locale": "en-US"
    }
  }
}

CSV with JSON String

id,name,metadata
1,Alice,"{""tags"":[""vip"",""priority""],""preferences"":{""theme"":""dark"",""locale"":""en-US""}}"

Pros & Cons

✓ Advantages

  • • 100% data preservation
  • • Minimal columns
  • • Perfectly reversible
  • • Good for rarely-accessed data

✗ Disadvantages

  • • Can't filter/sort nested data in Excel
  • • Requires parsing to access
  • • Not human-friendly
  • • Quote escaping complexity

Strategy 4: Multiple Tables (Relational)

Split into related tables with foreign keys, like a relational database.

Example

Input JSON

{
  "orderId": "A123",
  "customer": "Alice",
  "items": [
    {"product": "Laptop", "price": 999},
    {"product": "Mouse", "price": 25}
  ]
}

Output: Two CSVs

orders.csv

orderId,customer
A123,Alice

order_items.csv

orderId,product,price
A123,Laptop,999
A123,Mouse,25

Pros & Cons

✓ Advantages

  • • Normalized data (no duplication)
  • • Efficient storage
  • • Database-compatible structure
  • • Scales well

✗ Disadvantages

  • • Multiple files to manage
  • • Requires JOIN operations to analyze
  • • More complex setup
  • • Not suitable for Excel sheets

Choosing the Right Strategy

Use CaseBest StrategyWhy
Simple nested objectsDot NotationClean, preserves structure
One-to-many relationshipsArray ExpansionNatural tabular format
Complex, rarely accessed dataJSON StringPreserves everything
Database-bound dataMultiple TablesNormalized, efficient
Excel analysisArray ExpansionPivot-table friendly

Handling Deep Nesting (5+ Levels)

⚠️ When JSON is Too Complex

If your JSON has 5+ nesting levels or multiple arrays at different depths, consider:

  • 1. Simplify the JSON: Pre-process to reduce complexity
  • 2. Hybrid Approach: Flatten important fields, preserve complex ones as JSON strings
  • 3. Keep as JSON: CSV might not be the right format

Practical Tips

  • 1.
    Know Your Audience: Excel users prefer expanded rows, developers prefer dot notation
  • 2.
    Test with Real Data: Edge cases often appear only with production data
  • 3.
    Document Your Approach: Future you will thank present you
  • 4.
    Consider Round-Trip: Can you convert back to original JSON if needed?

💡 Try Our Flattening Tool

Our converter supports multiple flattening strategies with live preview. Test different approaches to see which works best for your data.

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Authored by: JSON CSV Converter

Last updated: February 19, 2026