Anomaly Check Before Load
Detect anomalies, validate CSV shape, then convert to JSON for loading.
Use case
Use this before ETL/database import to catch issues early.
What to expect
Follow the steps from left to right for a quick overview, then use the inline stepper below to run each tool.
Detect anomalies, validate CSV shape, then convert to JSON for loading.
CSV Anomaly Detector
JSON → JSON
Detect anomalies
JSON report with missing values, type errors, outliers, and duplicate keys.
CSV Validator
CSV → TEXT
Validate CSV
Status report with column count and any detected errors.
CSV to JSON Converter
CSV → JSON
Convert to JSON
JSON generated from the CSV input.
Workflow steps
Workflow shortcut
Next unlocked step: Step 1 · CSV Anomaly Detector
CSV Anomaly Detector
Identify data quality issues in CSV: missing values, numeric outliers (IQR), type inconsistencies, and duplicate key rows.
Anomaly detector input (JSON envelope)
Provide { "csv": "...", "numericColumns"?: ["age"], "keyColumn"?: "id" }.
Anomaly report
JSON report with missing values, type errors, outliers, and duplicate keys.
Run this step to process the current input and prepare the next workflow stage.
CSV Validator
Validate CSV syntax and structure — checks column consistency, unclosed quotes, and empty headers.
CSV input
Paste CSV to validate. The original CSV is passed to the next step on success.
Validation result
Status report with column count and any detected errors.
Run this step to process the current input and prepare the next workflow stage.
CSV to JSON Converter
Convert CSV rows with headers into a clean JSON array.
CSV input
Paste CSV rows with a header row.
JSON output
JSON generated from the CSV input.
Run this step to process the current input and prepare the next workflow stage.