qc2plus 1.0.5

Data Quality Automation Framework with ML-powered anomaly detection

QC2Plus - Advanced Data Quality Framework

Production-ready data quality framework with ML-powered anomaly detection

Features • Installation • Quick Start • Documentation • Examples

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🎯 What is QC2Plus?

QC2Plus is an open-source Python framework for automated data quality testing, combining traditional SQL-based validation with advanced machine learning anomaly detection.

Two-Level Quality Approach

Level 1: SQL-Based Validation 🔍

• Business rules (unique, not_null, relationship)
• Format validation (email, phone, dates)
• Statistical thresholds (detect metric anomalies)
• Custom SQL tests

Level 2: ML-Based Anomaly Detection 🤖

• Correlation shifts between variables
• Temporal pattern changes
• Distribution drift across segments
• Smart contextual filtering

Why QC2Plus?

Feature	Traditional Tools	QC2Plus
Setup Time	Hours to days	Minutes
Anomaly Detection	Rule-based only	ML-powered
Alerting	Basic notifications	Multi-channel with context
Monitoring	Standalone	Power BI integration
Learning Curve	Steep	dbt-like CLI

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✨ Features

🚀 Easy to Use

• dbt-inspired CLI: Familiar qc2plus run, qc2plus test commands
• YAML Configuration: Simple model and test definitions
• Auto-Discovery: Automatically finds models in your project
• Multi-Environment: Separate configs for dev, staging, prod

🗄️ Database Support

Database	Support Level	Installation
PostgreSQL	✅ Stable	Included
Snowflake	✅ Stable	pip install qc2plus[snowflake]
BigQuery	✅ Stable	pip install qc2plus[bigquery]
Redshift	⚠️ Beta	pip install qc2plus[redshift]

📊 Comprehensive Testing

Level 1 Tests (8 built-in types):

• unique, not_null, accepted_values
• relationship, range_check
• email_format, future_date
• statistical_threshold (ML-powered)

Level 2 Analyzers (3 ML algorithms):

• Correlation Analyzer: Detect relationship changes
• Temporal Analyzer: Find time series anomalies
• Distribution Analyzer: Monitor segment shifts

🔔 Smart Alerting

• Channels: Email (SMTP), Slack, Microsoft Teams
• Severity Levels: Critical, High, Medium, Low
• Smart Routing: Individual alerts for critical, summaries for others
• Rich Formatting: HTML emails, Slack cards, Teams adaptive cards

📈 Power BI Ready

Three auto-created tables for instant dashboards:

• quality_test_results - Individual test outcomes
• quality_run_summary - Run-level metrics
• quality_anomalies - ML-detected anomalies with details

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📦 Installation

Installation

pip install qc2plus

🏁 Quick Start

1. Initialize Project

qc2plus init my_quality_project
cd my_quality_project

This creates:

my_quality_project/
├── qc2plus_project.yml    # Project config
├── profiles.yml            # Database connections
├── models/                 # Test definitions
│   └── customers.yml       # Example model
└── README.md               # Getting started guide

2. Configure Database

Edit profiles.yml:

my_quality_project:
  target: dev
  outputs:
    dev:
      data_source:              # Where your data lives
        type: postgresql
        host: localhost
        port: 5432
        user: ${DB_USER}        # Use env variables!
        password: ${DB_PASSWORD}
        dbname: analytics
        schema: public
      
      quality_output:            # Where results are stored
        type: postgresql
        host: localhost
        port: 5432
        dbname: quality_db
        schema: qc2plus

Security Best Practice: Use environment variables for credentials!

3. Define Tests

Edit models/customers.yml:

models:
  - name: customers
    description: Customer data quality tests
    
    qc2plus_tests:
      # Level 1: Business Rules
      level1:
        - unique:
            column_name: customer_id
            severity: critical
        
        - not_null:
            column_name: email
            severity: critical
        
        - email_format:
            column_name: email
            severity: high
        
        - accepted_values:
            column_name: status
            accepted_values: ['active', 'inactive', 'churned']
            severity: medium
        
        - statistical_threshold:
            metric: count
            threshold_type: relative
            threshold_value: 2.0     # 2 std deviations
            window_days: 30
            severity: high
      
      # Level 2: ML Anomaly Detection
      level2:
        correlation_analysis:
          variables: [lifetime_value, order_count, avg_order_value]
          expected_correlation: 0.8
          threshold: 0.2
        
        temporal_analysis:
          date_column: created_at
          metrics: [count, avg_lifetime_value]
          seasonality_check: true
        
        distribution_analysis:
          segments: [country, customer_type]
          metrics: [lifetime_value, order_count]
          date_colum: date_order

4. Run Tests

# Test connection
qc2plus test-connection

# Run all tests
qc2plus run --target dev

# Run specific model
qc2plus run --models customers --target dev

# Run only Level 1
qc2plus run --level 1

# Parallel execution (4 threads)
qc2plus run --threads 4

# Production run with fail-fast
qc2plus run --target prod --fail-fast

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🏁 Quick Start With Docker

1. Clone the repository

git clone https://github.com/kheopsys/qc2plus
cd qc2plus

2. Start all services

docker-compose up -d

Expected output:

 Container qc2plus-postgres         Started
 Container qc2plus-postgres-results Started
 Container qc2plus-runner           Started

3. Verify services are running

docker-compose ps

4. Access the QC2Plus container

docker exec -it qc2plus-runner bash

5. Inside the container, run quality checks

cd examples/advanced
qc2plus run --models customers --target demo

6. View results in PostgreSQL

docker exec -it qc2plus-postgres-results psql -U qc2plus -d qc2plus_results \
  -c "SELECT model_name, test_type, status, failed_rows 
      FROM quality_test_results 
      ORDER BY execution_time DESC 
      LIMIT 10;"

📚 Documentation

📖 Complete Guides

• Qc2plus Documentation - Complete parameter reference
• Examples - Real-world use cases

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📋 Test Reference

Level 1 Tests

Test	Use Case	Example
unique	Primary keys, unique identifiers	customer_id, email
not_null	Required fields	email, created_at
email_format	Email validation	Email addresses
relationship	Referential integrity	customer_id → customers.id
accepted_values	Enum/status fields	status in ['active', 'inactive']
range_check	Numeric boundaries	age between 0 and 120
future_date	Date validation	Birth dates, creation dates
statistical_threshold	Metric anomalies	Daily registrations, revenue

See documentation for complete parameter reference.

Level 2 Analyzers

Analyzer	Detects	Example Scenario
Correlation	Relationship changes	Marketing spend vs revenue decoupling
Temporal	Time series anomalies	Unexpected spike in daily signups
Distribution	Segment shifts	Geographic distribution change

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🔔 Alerting Example

Configure in qc2plus_project.yml:

alerting:
  enabled_channels: [slack, email]
  
  thresholds:
    critical_failure_threshold: 1    # Alert on 1+ critical failure
    failure_rate_threshold: 0.15     # Alert if >15% tests fail
  
  slack:
    enabled: true
    webhook_url: ${SLACK_WEBHOOK_URL}
  
  email:
    enabled: true
    smtp_server: smtp.gmail.com
    smtp_port: 587
    username: ${EMAIL_USERNAME}
    password: ${EMAIL_APP_PASSWORD}
    from_email: [e-mail]
    to_emails:
      - [e-mail]
      - [e-mail]

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📊 Power BI Integration

QC2Plus automatically creates three tables in your quality database:

1. quality_test_results

Individual test results with full details.

SELECT 
  model_name,
  test_name,
  test_type,
  level,
  severity,
  status,
  failed_rows,
  total_rows,
  execution_time
FROM qc2plus.quality_test_results
WHERE execution_time >= CURRENT_DATE - INTERVAL '30 days'
ORDER BY execution_time DESC;

2. quality_run_summary

High-level run metrics for trend analysis.

SELECT 
  run_id,
  execution_time,
  target_environment,
  total_tests,
  passed_tests,
  failed_tests,
  critical_failures,
  execution_duration_seconds
FROM qc2plus.quality_run_summary
ORDER BY execution_time DESC;

3. quality_anomalies

ML-detected anomalies with severity scores.

SELECT 
  model_name,
  analyzer_type,
  anomaly_type,
  anomaly_score,
  affected_columns,
  detection_time,
  severity
FROM qc2plus.quality_anomalies
WHERE detection_time >= CURRENT_DATE - INTERVAL '7 days'
ORDER BY anomaly_score DESC;

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🎯 Examples

E-commerce Data Quality

models:
  - name: orders
    qc2plus_tests:
      level1:
        - not_null:
            column_name: order_id
            severity: critical
        - relationship:
            column_name: customer_id
            reference_table: customers
            reference_column: id
            severity: critical
        - range_check:
            column_name: order_total
            min_value: 0
            severity: high
        - statistical_threshold:
            metric: sum
            column_name: order_total
            threshold_type: relative
            threshold_value: 3.0
            severity: high
      
      level2:
        correlation_analysis:
          variables: [order_total, item_count, shipping_cost]
          expected_correlation: 0.7
          threshold: 0.25
        
        temporal_analysis:
          date_column: order_date
          metrics: [count, sum_order_total, avg_order_total]
          seasonality_check: true

SaaS Metrics Monitoring

models:
  - name: daily_metrics
    qc2plus_tests:
      level1:
        - statistical_threshold:
            metric: count
            column_name: new_signups
            threshold_type: relative
            threshold_value: 2.0
            window_days: 30
            severity: high
        
        - statistical_threshold:
            metric: sum
            column_name: mrr
            threshold_type: absolute
            threshold_value: 100000
            severity: critical
      
      level2:
        correlation_analysis:
          variables: [new_signups, trial_starts, paid_conversions]
          expected_correlation: 0.85
          threshold: 0.15
        
        temporal_analysis:
          date_column: metric_date
          metrics: [new_signups, churn_count, mrr]
          seasonality_check: true
          window_days: 180

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│         QC2Plus Architecture                │
├─────────────────────────────────────────────┤
│                                             │
│  ┌──────────────┐     ┌──────────────┐     │
│  │   Level 1    │     │   Level 2    │     │
│  │  SQL Tests   │────▶│  ML Anomaly  │     │
│  │              │     │  Detection   │     │
│  └──────────────┘     └──────────────┘     │
│         │                     │             │
│         ▼                     ▼             │
│  ┌────────────────────────────────────┐    │
│  │      Results Persistence           │    │
│  │  (PostgreSQL/BigQuery/Snowflake)   │    │
│  └────────────────────────────────────┘    │
│         │                                   │
│         ├──▶ Power BI Dashboards            │
│         └──▶ Multi-Channel Alerts           │
│              (Slack/Email/Teams)            │
└─────────────────────────────────────────────┘

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🚀 Performance Tips

1. Parallel Execution: Use --threads based on DB capacity

   qc2plus run --threads 4  # Good for most setups
   

2. Optimize Windows: Adjust based on data volume

   window_days: 30  # Fast, less history
   window_days: 90  # Balanced
   window_days: 180  # Comprehensive, slower
   

3. Index Critical Columns: Especially date columns

   CREATE INDEX idx_created_at ON customers(created_at);
   

4. Use Sampling: For exploratory analysis

   min_samples: 1000  # ML tests skip if < 1000 rows
   

5. Schedule Wisely: Run during low-traffic periods

   # Crontab example: Daily at 2 AM
   0 2 * * * cd /path/to/project && qc2plus run --target prod
   

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🐛 Troubleshooting

Connection Issues

# Test database connection
qc2plus test-connection --target dev

# Enable debug logging
export QC2PLUS_LOG_LEVEL=DEBUG
qc2plus run

Tests Not Found

# List all models
qc2plus list-models

Performance Issues

# Reduce window for testing
statistical_threshold:
  window_days: 7  # Instead of 30

# Increase minimum samples
level2:
  temporal_analysis:
    min_samples: 100  # Skip analysis if < 100 rows

Memory Errors

# Reduce parallel threads
qc2plus run --threads 1

# Or increase Docker memory (if using Docker)
docker run --memory=4g qc2plus

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Start:

git clone https://github.com/kheopsys/qc2plus.git
cd qc2plus
pip install -e ".[dev]"
pytest tests/

Areas We Need Help:

• 📝 Documentation improvements
• 🧪 Additional test types
• 🗄️ New database adapters
• 🎨 Power BI templates
• 🌐 Translations

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📄 License

MIT License - see LICENSE for details.

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🙏 Contributors & Acknowledgments

Main Contributors

This project is maintained by:

Ikrame Ettiache Creator & Maintainer 🤖 💻 📊

Abdoul Raoufou Gambo Creator & Maintainer 💻 🐛 📖

Yasser Sokri Creator & Maintainer 🤖 💻 📊

Special Thanks

• Inspired by dbt for the elegant CLI approach
• Built with SQLAlchemy, scikit-learn, pandas
• Thanks to everyone who reported bugs and suggested features!

Sponsor

If QC2Plus helps your organization, consider:

• ⭐ Starring the repo
• 🐛 Reporting bugs
• 💡 Suggesting features
• 🤝 Contributing code

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📧 Support & Community

📧 Support & Community

• 📖 Documentation: QC2plus documentation
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 💼 LinkedIn: QC2Plus
• ✉️ Email: [e-mail]

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⭐ Star us on GitHub if QC2Plus helps your data quality! ⭐

Made with ❤️ by the QC2Plus Team

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