sentri 1.0.0

Sentri - a production-ready, configurable data quality validation framework

Sentri

Sentri is a production-ready, configurable data quality validation framework with 10 check types, multiple data connectors, and flexible output formats.

Features

• 10 Check Types: Completeness, Uniqueness, Range, Turnover, Value Spike, Frequency, Correlation, Statistical, Distribution, Drift
• Multiple Connectors: CSV, Oracle, Snowflake (extensible)
• Flexible Configuration: YAML with environment variable support
• Multiple Output Formats: JSON, HTML, CSV, DataFrame
• Threshold-Based Validation: Critical, Warning, Pass, Error states
• Comprehensive Logging: Text and JSON formats

Installation

pip install sentri

# Optional: development extras when working on the project itself
pip install -e ".[dev]"  # Development install
pip install -e ".[all]"  # With all database connectors

Quick Start

Programmatic Usage

from sentri import DataQualityFramework
from sentri.checks import CompletenessCheck, TurnoverCheck
from sentri.connectors import OracleConnector, SnowflakeConnector

# Example: using DataQualityFramework with a config file
framework = DataQualityFramework(config_path="config.yaml")
results = framework.run_checks(start_date="2025-01-01", end_date="2025-01-31")

Configuration File Usage

# config.yaml
source:
  type: csv
  csv:
    file_path: /data/sample.csv
    date_column: effective_date

metadata:
  dq_check_name: "Sample Check"
  date_column: effective_date
  id_column: entity_id

checks:
  completeness:
    value:
      thresholds:
        absolute_critical: 0.05
        absolute_warning: 0.02
      description: "Value completeness"

output:
  formats: [json, html, csv]
  destination: /output

Check Types

Check Type	Description
Completeness	Monitor null/missing values
Uniqueness	Detect duplicate values
Range	Validate value bounds
Turnover	Track ID additions/removals
Value Spike	Detect abnormal value changes
Frequency	Monitor category distributions
Statistical	Track mean, std, median, etc.
Correlation	Validate temporal/cross-column correlation
Distribution	Detect distribution shifts (KS test)
Drift	Identify gradual drift (PSI)

Project Structure

dq_framework/
├── src/data_quality/
│   ├── checks/           # Check implementations
│   ├── connectors/       # Data connectors
│   ├── core/             # Exceptions, config, framework
│   ├── formatters/       # Output formatters
│   ├── managers/         # Check manager
│   └── utils/            # Logger, constants
├── tests/                # Unit tests
├── examples/             # Sample configs and scripts
└── pyproject.toml

Running Tests

pytest tests/unit/ -v --cov

Thresholds

Each check supports:

• absolute_critical: Fails if exceeded
• absolute_warning: Warns if exceeded
• delta_critical/delta_warning: For change-based thresholds

Output

Results include:

• Summary: Total, passed, warnings, failed, pass rate
• Details: Check type, column, date, metric value, status

License

MIT