JSON converter

JSON is a lightweight data interchange format made of objects, arrays, strings, numbers, booleans, and null. It is the default language of web APIs, configuration files, logs, app exports, test fixtures, and automation. JSON is structured but not tabular by default: an array of objects can look like rows, while deeply nested objects, variable arrays, and mixed types do not fit cleanly into CSV or spreadsheets. Conversion begins by identifying the record boundary. Is the whole file one object, an array of records, a map keyed by IDs, or a stream of separate JSON values?

Variants matter. Standard JSON is one complete value and does not allow comments or trailing commas. JSON Lines, also called JSONL or NDJSON, stores one JSON object per line and is better for logs and streaming pipelines. Some configuration formats marketed as JSON allow comments or relaxed syntax, but strict parsers reject them. Numbers are another trap: JSON has one number type, while JavaScript and many parsers represent it as IEEE 754 double precision. Very large integers, account IDs, and decimal money values can lose precision unless kept as strings or parsed by a decimal-aware tool. Dates are strings by convention, not a native type.

JSON is read and written by browsers, Node, Python, Go, Java, databases, warehouses, analytics tools, API gateways, no-code platforms, data labeling systems, and cloud logs. These systems differ in schema expectations. An API may require camelCase keys and ISO timestamps. A warehouse may prefer flattened columns and stable types. A spreadsheet user may need one row per order, while the JSON has one order containing an array of line items. The converter must either preserve nested structure for JSON-to-JSON transformations or make flattening decisions visible when targeting CSV, XLSX, SQL, or Markdown.

Conversion gotchas center on shape loss. JSON to CSV flattens nested object paths into columns and must decide how arrays are represented: strings, indexed columns, or exploded rows. CSV to JSON can create strings for everything unless type inference is requested, and type inference can damage identifiers. JSON to YAML may preserve structure but change quoting and multiline style. JSON to XLSX is convenient for analysts but can hide nested complexity. Before converting, sample records with missing keys, nulls, large numbers, nested arrays, and unexpected types. For production, pair the JSON with a schema or contract and reject records that drift silently.

For reliable JSON conversion, separate validation from presentation. First parse the file strictly and confirm that every record follows the shape you expect. Then decide the target model: nested JSON for APIs, JSONL for streaming, flattened CSV for analysts, or XLSX for review. Keep a copy of the raw JSON when auditability matters because flattened outputs cannot always be reversed. Document path naming, array handling, date conventions, and numeric precision. When integrating with APIs, test with empty arrays, null fields, unknown keys, and maximum-size payloads. Those edge cases expose whether the conversion is a one-time convenience or a stable production contract with explicit failure behavior. Schema examples should include bad records too.

Official JSON specification