JSON to CSV for Spreadsheets: Nested Data, Arrays, and Missing Values

Choose the record array

The cleanest source is a non-empty array of objects where each object represents one row. If records are nested below a wrapper property, isolate that array before conversion.

Preserve source order when row sequence matters. UtilityDock creates headers from discovered fields and keeps record order.

Flatten nested objects deliberately

A one-level object can become dot-notation columns such as customer.name. Deeper objects and arrays need an explicit policy: serialize them as JSON text, flatten them further, or extract only selected values.

[
  { "id": 1, "customer": { "name": "Ada" }, "tags": ["new", "trial"] }
]

id,customer.name,tags
1,Ada,"[""new"",""trial""]"

Handle missing, null, and empty values consistently

Missing means a property is absent, null is an explicit JSON value, and an empty string is present text with zero characters. Spreadsheets may display all three as blank, so inspect completeness before conversion when the distinction matters.

Use Null & Missing Value Inspector to quantify each case and Field Selector to keep only columns required for the export.

Verify the spreadsheet boundary

CSV escaping must protect commas, quotes, and line breaks. Open the preview, confirm headers and representative rows, then download. If a spreadsheet guesses dates or large numbers incorrectly, import the CSV with explicit column types.

1. Validate the JSON array.
2. Inspect missing and nested fields.
3. Select or flatten the required paths.
4. Convert and preview the CSV.
5. Import with explicit spreadsheet types when precision matters.

Common questions

FAQ