Detect mixed data types and format inconsistencies within CSV columns

Schema drift accumulates silently. An amount column that started as plain integers acquires "N/A" strings from one team, "n/a" from another, and decimal fractions from a third. A date column receives entries in ISO 8601 from the engineering export, MM/DD/YYYY from the US sales team, and DD/MM/YYYY from the UK office. Running SUM or GROUP BY against a column with mixed types produces a type error at best, or silently wrong numbers at worst — and both outcomes are difficult to trace back to their cause without a consistency report. DataFix profiles each column across all rows, determines the dominant type (string, integer, float, date, boolean), and reports every row that deviates from it. This is a diagnostic tool: it tells you where type inconsistencies exist so you can decide the appropriate fix. Jagged CSV files—where specific rows have fewer or more delimiters than the header row—are a leading cause of pipeline failures in cloud data warehouses like Snowflake, BigQuery, and Databricks. Inconsistent column counts typically occur when unescaped commas exist in user-submitted comments or when line breaks break row boundaries. DataFix audits every row against the header dimensionality, flagging rows with field mismatches, displaying expected versus actual column counts, and isolating the exact line numbers where parsing breaks down. Uneven field counts caused by unescaped line breaks or rogue commas corrupt downstream data pipelines, causing batch jobs to fail in Snowflake or BigQuery. Highlighting rows with ragged delimiter counts pinpoints unescaped commas before database loaders abort the entire batch.

What types are checked?

Integer, float, date, boolean, and text. Mixed-type columns are flagged.

Does it auto-fix inconsistencies?

Not automatically. The report shows which rows and columns are inconsistent. Use Find and Replace or type conversion tools to fix.

Validate

Validate CSV

Audit CSV files for structural errors, encoding problems, and row-length inconsistencies.

Open Tool →

Validate

Validate JSON

Parse and validate JSON strings to expose syntax errors at exact line and column positions.

Open Tool →

Validate

Find Duplicate Rows

Identify and count duplicate rows in CSV files by exact match or by key column selection.

Open Tool →