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Illustrative Data Cleaner workflow

Data Cleaner

Clean and validate data.

Find duplicates, nulls, and broken rows. Clean a copy. Leave the original file untouched.

Who it is for: Operations and data teams with local or mounted tabular files.

  1. Upload
  2. →Profile
  3. →Review plan
  4. →YES/NO for risky ops
  5. →Execute
  6. →Validate
  7. →Download

Operating model · INPUT → UNDERSTAND → CLEAN → ANALYZE → MODEL → DECIDE → DELIVER

Learn moreStart Data Cleaner

What it solves

  • Profile local CSV, TSV, or JSONL and see real issue counts.
  • Build a deterministic cleaning plan before anything is written.

Who it is for

  • Operations and data teams with local or mounted tabular files.

What you provide

  • Browser upload of CSV, TSV, JSONL, NDJSON, or Parquet
  • XLSX workbooks use the Excel Work Engine
  • Parquet when the Python backend is configured

Requirement

  • A job statement — what you need this product to do with the input you provide.

What ORINEL does

  • Profiling
  • Issue detection
  • Plan generation
  • Execution after confirmation
  • Independent validation

How it works

  • Upload
  • Profile
  • Review plan
  • YES/NO for risky ops
  • Execute
  • Validate
  • Download

What you receive

  • A new cleaned file
  • Job audit
  • Row and byte counts from the run

Examples

  • Remove exact duplicate rows from a sales extract.
  • Normalize null tokens and blank rows on a CSV.

AUTO mode

  • Upload the file and the requirement. ORINEL profiles, plans, and cleans a new copy after you confirm risky steps.

EXPERT mode

  • Review the plan operations, confirmation gates, and validation report before download.

Implemented capabilities

  • Streaming profile of CSV, TSV, and JSONL
  • Deterministic clean plan from the real profile
  • Independent output validation before completed
  • Original file is never overwritten

Security and trust

  • One ORINEL account and the existing authentication system.
  • Workspace routes require a signed-in session.
  • Safe post-login redirects stay on this origin.
  • Uploads are tenant-scoped. The original file is never overwritten.

Limitations

Parquet requires the Python AI backend. S3, Azure, GCS, databases, XLSX, directories, and distributed / 50 TB execution are not configured.

Start Data CleanerAll products

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