Dift

What is Dift?

Dift is an open-source platform for dataset comparison, drift detection, and automated data trust validation.

It helps data teams instantly understand:

• what changed
• why it matters
• whether new data is safe to trust

Dift supports:

• local datasets
• SQL databases
• analytical warehouses
• drift analysis
• automation workflows
• historical validation
• reusable comparison systems

---

Documentation

Full documentation is available here:

https://reginalderzoah.github.io/Dift/

---

Why Dift?

Bad data silently breaks:

• dashboards
• ETL pipelines
• analytics workflows
• ML models
• warehouse transformations
• business decisions

Dift helps teams detect risky dataset changes before they propagate into production systems.

---

Key Features

Dataset Comparison

• Schema comparison
• Row-level comparison
• Null analysis
• Duplicate analysis
• Risk scoring
• Drift analysis

---

Drift Detection

Numeric Drift

• Mean shift detection
• Standard deviation drift
• Range shift analysis
• Severity classification

Categorical Drift

• New value detection
• Removed value detection
• Frequency shift analysis
• Severity scoring

Outlier Detection

• IQR outlier analysis
• Outlier spike detection
• Risk integration

---

Supported Dataset Sources

Local Files

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

Databases & Warehouses

• SQLite
• PostgreSQL
• MySQL
• DuckDB
• BigQuery
• Redshift
• Snowflake

---

Reporting

Dift supports:

• Rich CLI reports
• JSON reports
• CSV reports
• Excel reports
• HTML reports

---

HTML Templates

Available templates:

• default
• clean
• compact
• enterprise
• dark

Example:

dift old.csv new.csv \
  --report html \
  --template dark

---

Automation Features

• Scheduled comparisons
• Batch dataset comparison
• Comparison history
• Reusable profiles
• Environment-based configs
• Automation-friendly exit codes
• Non-interactive execution

---

Installation

Install

pip install dift-cli

---

Upgrade

pip install --upgrade dift-cli

---

Optional Connector Dependencies

SQL Support

pip install sqlalchemy

PostgreSQL

pip install psycopg2-binary

MySQL

pip install pymysql

Redshift

pip install sqlalchemy-redshift redshift-connector

Snowflake

pip install snowflake-sqlalchemy

BigQuery

pip install google-cloud-bigquery db-dtypes

DuckDB

pip install duckdb

---

Quick Start

Compare CSV Files

dift examples/old.csv examples/new.csv \
  --key customer_id

---

Generate JSON Report

dift examples/old.csv examples/new.csv \
  --key customer_id \
  --report json \
  --output report.json

---

Generate HTML Report

dift examples/old.csv examples/new.csv \
  --key customer_id \
  --report html \
  --template enterprise \
  --output report.html

---

Detect Numeric Drift

dift examples/old_drift.csv examples/new_drift.csv \
  --key id \
  --threshold 0.1

---

Database & Warehouse Examples

PostgreSQL

dift postgresql://user:password@localhost:5432/sales_db:customers_old \
     postgresql://user:password@localhost:5432/sales_db:customers_new \
     --key customer_id

---

DuckDB

dift duckdb:///warehouse.duckdb:orders_old \
     duckdb:///warehouse.duckdb:orders_new \
     --key order_id

---

BigQuery

dift bigquery://analytics.sales.orders_old \
     bigquery://analytics.sales.orders_new \
     --key order_id

---

Batch Comparison

dift batch \
  --old-dir data/old \
  --new-dir data/new \
  --key id

---

Scheduled Workflows

Create Profile

dift profile create nightly-check \
  --old examples/old.csv \
  --new examples/new.csv \
  --key customer_id

Run Profile

dift profile run nightly-check

Generate Cron Schedule

dift schedule cron nightly-check

---

Comparison History

Enable persistent history tracking:

dift examples/old.csv examples/new.csv \
  --history

---

Automation-Friendly Execution

dift prod.csv staging.csv \
  --strict-exit-codes \
  --quiet \
  --no-color

---

Configuration Support

Supported config formats:

• YAML
• TOML
• JSON

Run using config:

dift --config examples/config_sample.yaml

---

Environment-Based Configs

dift --config examples/config_env.yaml \
  --env production

---

Example Files

Most examples use files located in the project's examples/ directory.

Example structure:

examples/
├── old.csv
├── new.csv
├── old.parquet
├── new.parquet
├── old.xlsx
├── new.xlsx
├── old.json
├── new.json
├── old_drift.csv
├── new_drift.csv
├── config_sample.yaml
├── config_thresholds.yaml
├── config_env.yaml
└── warehouse.duckdb

---

Project Structure

dift/
├── cli.py
├── core/
├── io/
│   ├── readers.py
│   ├── registry.py
│   ├── sql_reader.py
│   ├── duckdb_reader.py
│   ├── bigquery_reader.py
│   └── base_reader.py
├── reports/
├── profiles.py
├── schedules.py
├── history.py
└── utils/

docs/
tests/
examples/

---

Developer Features

• Connector registry architecture
• Shared reader interfaces
• Plugin preparation architecture
• Modular connector system
• Extensible reporting system
• Warehouse-ready workflows

---

Run Tests

pytest

---

Linting

ruff check .

---

Type Checking

mypy dift

---

Roadmap

Upcoming areas of focus include:

• streaming comparisons
• distributed execution
• MongoDB support
• ML feature drift analysis
• observability dashboards
• alerting workflows
• native Airflow integration
• plugin ecosystem
• Python SDK
• Web UI dashboard

See:

docs/roadmap.md

---

Contributing

Contributions are welcome.

See:

CONTRIBUTING.md

Ways to contribute:

• fix bugs
• improve docs
• improve testing
• improve performance
• add connectors
• improve reporting
• improve automation workflows

---

Vision

Dift aims to become the open-source standard for:

• dataset regression testing
• data drift monitoring
• ML dataset validation
• warehouse trust validation
• automated data quality enforcement
• data deployment validation
• dataset observability