Data & Format Tools

Working with Data & Format Tools

Working with structured data usually goes wrong at the edges rather than in the obvious places. A JSON object may be valid but still use the wrong types, a CSV may look fine until a quoted comma shifts a column, and YAML can change meaning because of indentation. The tools in this section are useful when you need to inspect, reformat, compare, convert, or clean data without setting up a local script for a one-off task.

Before you transform anything, keep a copy of the original input and check whether numbers, booleans, null values, dates, and nested fields survive the round trip. When converting between formats, pay particular attention to duplicate keys, empty cells, line endings, character encoding, and fields that contain delimiters. A result that parses successfully is not always a result that preserves the original structure.

Practical tips

• Validate first, then format or convert. Fixing malformed input after several transformations is much harder.
• Check a small representative sample before running a large payload through multiple conversions.
• When moving between CSV and JSON, decide whether blank cells should become empty strings, nulls, or missing fields.
• Keep key order only when it matters for human review; most data formats do not treat order as meaning.
• For production data, compare record counts and a few known values before replacing the source file.