Column Mapping and Renaming in Bulk INSERT

Column Mapping: Reshape Your Data

The column mapping feature lets you transform JSON keys into SQL column names, exclude columns you do not need, and control exactly what appears in the INSERT output.

Renaming Columns

JSON APIs often use camelCase, while SQL databases prefer snake_case:

JSON Key	SQL Column
firstName	first_name
lastName	last_name
emailAddress	email_address
createdAt	created_at

The tool shows each JSON key with an editable text field where you can type the desired SQL column name.

Excluding Columns

Some JSON fields may not belong in your SQL table:

• Internal IDs or metadata (_id, __v, _etag)
• Computed fields (full_name, age)
• Redundant data (created_by_name when you have created_by_id)

Uncheck columns in the mapping section to exclude them from the INSERT output. Use "Select All" and "Deselect All" to quickly manage large column sets.

Example

Given this JSON:

[
  { "userId": 1, "firstName": "Alice", "lastName": "Smith", "_internal": "xyz", "score": 95 }
]

With mapping: userId → user_id, firstName → first_name, lastName → last_name, _internal excluded, score included as-is:

INSERT INTO "students" ("user_id", "first_name", "last_name", "score")
VALUES
  (1, 'Alice', 'Smith', 95);

Type Inference with Mapping

Renaming a column does not affect type inference. The tool infers types from the JSON values regardless of what you name the SQL column. If you need to override a type, use the CREATE TABLE toggle and manually edit the output.

Preserving Mappings

Column mappings persist as long as the JSON structure remains the same. If you update the JSON input but keep the same keys, your custom column names and inclusion/exclusion choices are preserved.