Clean Schema Drift Detector Online
Compare two CSV or JSON samples for added, removed, renamed, type, nullability, and enum drift. Use Schema Drift Detector in your browser with no signup, no daily limit, and no backend processing for tool input.
Problem
You need to remove common data-quality problems before the file is imported, analyzed, converted, or shared.
What to do
Cleaning should be deliberate and reviewable. Start with safe fixes, inspect the result, and keep a copy of the original source data.
- 1Load the dataset into Schema Drift Detector.
- 2Choose the cleanup rules that match the problem.
- 3Preview the changes and export the cleaned data.
Use the browser tool
Schema Drift Detector compares two samples of the same dataset or API response and reports precisely what changed about its shape: fields added or removed, fields renamed, types changed, columns that started or stopped accepting nulls, and enum values that appeared or disappeared. It exists for the failure mode where nothing errors — the pipeline keeps running, the numbers quietly go wrong, and the change that caused it happened three weeks earlier upstream.
People also ask
- What is schema drift?
- Any change to the structure of incoming data — new or removed fields, changed types, changed nullability, new categorical values. It is a problem specifically because it usually does not raise an error; the pipeline keeps running on data that no longer means what it did.
- Why is a type change more dangerous than a removed field?
- A removed field usually breaks something loudly and gets fixed. A type change from integer to string often parses fine and silently makes sums and averages wrong, which can go unnoticed for months.
- How large should my samples be?
- Large enough that rare values appear. Nullability and enum drift are both properties of the value distribution, so a small sample will miss them and report false stability.