JSON Mapper

Tutorial

Stop writing manual transformation code. Paste two JSON examples and let the tool build transformation map automatically. Create one and reuse it anytime. All processing happens in your browser — nothing is sent to a server. Watch Tutorial video and Features video

1. Load Source and Target JSON

   Open the Mapper and fill both panels with example JSON — paste two JSON structures that define the formats you want to convert between.

   • Source (left panel) — the format you are transforming from. Use Open to load a .json file, Load from URL (Registered Users) to fetch one, or paste directly into the textarea.
   • Target (right panel) — the desired output format. Use the same loading options.
   • Click Simplify on either panel to merge duplicate array objects and keep only unique elements — to generate Transformation Map unique elements are needed.

   

2. Parse JSON(s)

   Click Parse JSON(s) to analyze both structures and open the Manual mapping editor.

   Fields are linked automatically in this priority order:

   1. Identical paths — source and target paths match exactly
   2. Identical values — fields contain the same example value (e.g. set both firstName and first_name to John before parsing)
   3. Matching short names — field names correspond after ignoring separators/case
   4. Common value formats — fields share the same detected format (e.g. price, date, email, url, word)

   

3. Adjust manual mappings

   Review links in the Manual mapping panel and connect any fields that were not matched automatically.

   • Source-first: click a source path, then pick an unlinked target from the list that appears.
   
   • Target-first: click a target path, then click the desired source path.
   

   To remove links:

   • Remove source — click the delete button (left) to drop a source path from the mapping. The target becomes available again. Use Parse JSON(s) to restore removed source paths.
   • Unlink target — click the chain button (center) to unlink the target while keeping the source path.

4. Build Map

   When all fields are linked, click Build Map. The generated transformation map appears in the Transformation Map panel.

   You can edit the map directly in that textarea. The format uses one source -> target mapping per line:

   catalog.categories[*].id -> catalog[*].short_name
   catalog.categories[*].products[0].currency -> catalog[*].price_currency
   catalog.categories[*].products[*].id -> catalog[*].products[*].id
   catalog.categories[*].products[*].name -> catalog[*].products[*].name
   catalog.categories[*].products[*].tags[*] -> catalog[*].products[*].plan.features[*]

   Use Open / Save on the Transformation Map panel to load or store a map for reuse (Subscribers only).

5. Apply Map and save output

   Replace the Source panel content with the real JSON data you want to transform, then click Apply Map. The transformed result is written to the Target panel.

   Click Save on the Target panel to download the output as a .json file.

Open Mapper →

Key Features & Capabilities

The JSON Mapper tool provides a powerful, code-free environment for transforming complex data structures. Below are the core features that enable seamless data integration and reorganization.

Tired of manually mapping complex JSON structures? JSON Mapper automates the bridge between your source data and your desired output format. By analyzing two JSON examples, the tool intelligently suggests links based on identical paths, matching names, shared values, and common value formats.

1. Intelligent JSON Key Renaming

   The tool allows for seamless renaming of keys across any nesting level. By mapping a source field to a target field with a different name (e.g., sku → product_code), the mapper automatically handles the translation. This is essential for normalizing data from different third-party APIs (like Stripe, Shopify, or Salesforce) into a consistent internal format.

2. Dynamic Structural Reorganization

   Beyond simple renaming, the tool can completely change the object's structure. You can move fields from a flat root level into a nested object (e.g., firstName → profile.personal.name) or extract deeply nested values into a flat structure. This "unflattening" and "flattening" capability allows you to reshape JSON to match specific database schemas or UI requirements without writing custom transformation code.

3. Multi-Level Array Flattening

   One of the most powerful features is the ability to convert multi-level nested arrays into a single-level list. For example, if you have a structure like Categories > Products > Tags, the mapper can "flatten" this so that every tag becomes a top-level item in a target array. During this process, the tool automatically propagates parent data (like the Category Name) into every child element, ensuring no context is lost.

4. Wildcard-Based Array Processing

   Using the [*] wildcard pattern, the tool can process arrays of any length. It identifies structural patterns within arrays, allowing you to map every object in a source list to a new structure in the target list. This ensures that whether your source has one item or one thousand, the transformation logic remains consistent and accurate.

5. Positional vs. Structural Mapping

   The mapper distinguishes between "structural" mappings (where every item in an array follows the same rule) and "positional" mappings (where you want to extract a specific element, such as tags[0] mapping to primary_category). This provides the flexibility to handle both bulk data processing and specific field extraction in a single transformation map.

6. Type Conversion and Value Mapping

   The tool supports converting data types during the mapping process, such as turning a simple array of strings into an array of complex objects, or vice versa. By "mapping by example," the tool infers the desired target type based on the target JSON you provide, automating the conversion of primitive values into structured data.

7. Automated Mapping Discovery

   The Parse JSON functionality automatically detects identical or similar field names, shared example values, and common value formats between the source and target files. It generates an initial set of mapping rules instantly, significantly reducing the manual effort required to build a transformation map for large, complex JSON files.

Open Mapper →

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