Data tool

Dataset Corruptor

Damage CSV, TSV, JSON, or plain-text datasets on purpose so you can test importers, validators, ETL jobs, and cleanup workflows against messy data.

Runs Locally

This tool runs in your browser. Files never leave your device.

Dataset input

Drop a JSON, CSV, TSV, or TXT file here or paste data below.

Open file

Drag and drop a dataset file anywhere inside this input card.

Corrupted output

Detected CSV · 5 source lines · 242 B output

FormatDamage presetSeed

Drop rows/items7%Duplicate rows/items7%Shuffle rows/items15%Blank values10%Mutate values9%Change types8%Remove fields/columns6%Rename fields/headers6%Insert junk4%Break structure4%

id,na,email,plan,active,score,created_at
1002,Jane Doe,jane@example.com,Free,,74,2026-06-03
1003,Sam Smith,sam@example.com,Team,true,88,2026-06-04
,Avery Stone,avery@example.com,Pro,true,91,
1001�,false,laramie@example.com,,true,98,2026-06-01

Corruption report

Rows dropped

Rows duplicated

Rows shuffled

Values blanked

Values mutated

Types changed

Fields removed

Fields renamed

Junk inserted

Structure damage

What this breaks

Imports: missing rows, duplicated rows, changed delimiters, blanks, and broken structure.

Schemas: removed fields, renamed keys, changed value types, null-ish values, and malformed output.

Cleaning: junk characters, weird casing, truncated strings, fake nulls, and inconsistent values.

Testing: seeded output lets you reproduce the same messy dataset during debugging.

What this tool does

Dataset Corruptor introduces controlled errors into structured datasets. Generate missing values, malformed records, noisy entries, and invalid data for testing machine learning pipelines, validation systems, parsers, and import workflows. The tool helps developers evaluate how systems behave when data quality is less than perfect.