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Dift v0.2.1 Release Notes

Release Date: Apr 28, 2026

Dift v0.2.1

Dift v0.2.1 focuses on improving usability, report quality, dataset validation workflows, and overall CLI stability.

This release strengthens the foundation established in v0.1.0 by improving comparison visibility, report consistency, and user experience across local dataset workflows.

Highlights

Dift v0.2.1 introduces:

  • improved comparison reporting
  • enhanced console output
  • stronger validation workflows
  • improved report consistency
  • better dataset handling
  • improved risk visibility
  • CLI workflow refinements
  • stability improvements

New Features

Improved Console Reports

Console reports now provide clearer visibility into:

  • schema changes
  • row differences
  • quality warnings
  • overall risk levels

The terminal output is now easier to scan and interpret during comparisons.

Enhanced Risk Visibility

Risk summaries have been improved to make risky dataset changes easier to identify quickly.

Examples include:

  • clearer warning sections
  • improved severity presentation
  • better comparison summaries

Improved HTML Reports

HTML reporting received several usability improvements:

  • cleaner layouts
  • improved section organization
  • better warning visibility
  • improved readability

Improved Excel Reports

Excel reports now include:

  • improved worksheet formatting
  • cleaner summary sections
  • better comparison organization

Better JSON Report Consistency

JSON reports were improved for:

  • cleaner serialization
  • more predictable structure
  • automation compatibility

This improves downstream integrations and machine-readable workflows.

Validation Improvements

Validation workflows were improved across the CLI.

Enhancements include:

  • clearer missing dataset handling
  • improved unsupported format errors
  • better user guidance
  • improved validation consistency

Dataset Handling Improvements

Dataset loading workflows were refined for:

  • improved reliability
  • cleaner error handling
  • more stable comparison execution

CLI Improvements

CLI usability improvements include:

  • better help output
  • improved command guidance
  • clearer report workflows

Stability Improvements

This release includes multiple internal improvements focused on:

  • comparison reliability
  • report generation stability
  • error handling consistency
  • maintainability

Supported Dataset Formats

Supported formats remain:

  • CSV
  • Parquet
  • Excel (.xlsx, .xls)
  • JSON

Report Formats

Supported report outputs:

  • console report
  • JSON report
  • CSV summary report
  • Excel workbook report
  • HTML report

Example Usage

Basic comparison:

dift old.csv new.csv --key customer_id

Generate HTML report:

dift old.csv new.csv \
  --key customer_id \
  --report html \
  --output report.html

Generate Excel report:

dift old.csv new.csv \
  --key customer_id \
  --report excel \
  --output report.xlsx

Example Output

╭─────────────────────────╮
│ Dift Dataset Comparison │
│ Risk Level: MEDIUM      │
╰─────────────────────────╯

Internal Improvements

Internal improvements include:

  • cleaner report rendering workflows
  • improved comparison organization
  • more maintainable report handling
  • better validation structure

Developer Experience

Development workflows continue to support:

pytest
ruff check .

Installation

Install from PyPI:

pip install dift-cli

Upgrade:

pip install --upgrade dift-cli

Known Limitations

Current limitations:

  • no SQL database connectors yet
  • no warehouse integrations yet
  • no batch comparison workflows
  • no scheduling system yet
  • no saved comparison profiles yet

These capabilities are planned for future releases.

Looking Ahead

Future releases will focus on:

  • SQL database support
  • warehouse integrations
  • automation workflows
  • reusable configurations
  • advanced drift analysis
  • scheduling systems

Thank You

Thank you to everyone contributing feedback, testing workflows, and helping improve Dift during its early growth phase.

Dift continues evolving toward becoming an open-source standard for dataset trust validation and drift detection.