Data Anonymizer
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Get Started

Start anonymizing in minutes

Try the demo, download the Free edition, and follow step-by-step guides for CLI or Docker usage.

1) Quick start

Pick a fast path

Use the demo to validate behavior, then download the Free edition for real runs in your environment.

Try the live demo

Explore rules and anonymization behavior in a controlled environment — no installation required.

Download the Free edition

Get the latest build from our release channel. We provide wheel and Docker delivery (no binaries embedded on this site).

Free edition workload limits
  • • Single file: up to 10 MB
  • • Archives: up to 50 MB (compressed)
  • • Extracted content: up to 200 MB total
  • • Extracted files: up to 200 files
Pro is intended for larger operational datasets and support bundles (limits removed).
2) Download options

Wheel or Docker

Choose the packaging format that matches your deployment constraints.

Python wheel

Best for standard Python environments, CI jobs, or controlled servers where Python is available.

  • • Install via `pip` from a release artifact.
  • • Runs CLI and (optionally) the UI, depending on your distribution.
  • • Works well for air-gapped workflows with mirrored artifacts.
  • • Free edition enforces workload limits; Pro removes them.
Download: releases
Docker image

Best for reproducible runs, isolation, and consistent execution across environments.

  • • Mount input/output/config folders.
  • • Run the CLI in ephemeral containers.
  • • Works well with private registries and restricted networks.
  • • Free edition enforces workload limits; Pro removes them.
3) Installation guides

Step-by-step onboarding

Use these as a starting point. Adapt paths and configuration to your environment.

CLI (wheel) guide
  1. 1. Download the wheel from the latest release folder.
  2. 2. Install in a virtual environment.
  3. 3. Run against an input folder (and optionally a rules/config file).
python -m venv .venv
source .venv/bin/activate
pip install ./data_anonymizer-*.whl

# Example run (adapt flags and paths to your environment)
data-anonymizer --input ./input --output ./output
Tip: start with a small sample, validate output, then scale to support bundles and archives.
Docker guide
  1. 1. Pull from your registry (public or private).
  2. 2. Mount folders for input/output and configuration.
  3. 3. Run the container to anonymize a bundle.
docker pull https://registry.example.com/ORG/data-anonymizer:latest

# Example run (adapt volume mounts and args)
docker run --rm \
  -v "$PWD/input:/work/input" \
  -v "$PWD/output:/work/output" \
  https://registry.example.com/ORG/data-anonymizer:latest \
  data-anonymizer --input /work/input --output /work/output
Tip: keep configuration in version control and mount it read-only for repeatable runs.
4) Documentation

Guides for users and administrators

Use the user guide to run anonymization safely, and the admin guide to operate at scale.

User Guide

Rules, best practices, validation workflows, and common patterns for logs and exports.

Admin Guide

Deployment patterns, private environments, reproducibility, and operational considerations.

5) Upgrade to Pro

Unlock Pro features with a license key

Pro features are enabled by a valid, signed license key. Validation happens locally.

How it works

After procurement, you receive a signed key. When present and valid, the application automatically enables premium features. If the key is missing, invalid, or expired, it runs in Free mode.

For security review context and deployment constraints, see Security & Licensing.

Get a Pro evaluation

If you need advanced capabilities (preview, profiling, performance options, or enterprise deployment), contact sales for an evaluation license and onboarding guidance.

We intentionally avoid prescribing a single “license install” command here: enterprises have different policy requirements. We provide integration guidance per environment.