data-validation-gini 0.3.12

Data Validation Gini (DVG) CLI for row count and row/column comparison with HTML reports

Data Validation Gini (DVG)

Data Validation Gini is a lightweight Python CLI for validating source and target datasets and generating a rich HTML reconciliation report.

The repository also includes a CSV data mutation utility (data_corruptor.py) to create controlled mismatches for validation testing.

Latest Updates

• Migrated to a src/ package layout (data_validation_gini) while preserving root-level compatibility wrappers.
• Enhanced CLI contract with explicit source/target kind flags (--src-kind, --tgt-kind) and compatibility shims.
• Added canonical validation-type normalization (ROWCOUNT alias -> ROWCOUNT_VALIDATION).
• Added mismatch capping with --max-mismatches.
• Added reusable file I/O classes:
  • IniConfigStore for INI read/write operations
  • JsonFileStore for JSON read/write operations
• Refactored test and coverage scripts for reliable local execution on Windows and Linux/macOS.
• Expanded automated tests and achieved 100% package coverage for data_validation_gini.

What This Project Does

• Compares source vs target files using row-level and cell-level checks.
• Supports CSV and Excel (.xlsx, .xlsm, .xltx) inputs.
• Supports single-sheet and multi-sheet validation (via sheet mapping).
• Produces a styled, filterable HTML report with KPI summary cards.
• Includes repeatable batch scripts for common mutation and validation scenarios.

Current Validation Modes

• ROWCOUNT_VALIDATION: checks source/target data row counts.
• ROWCOUNT: compatibility alias of ROWCOUNT_VALIDATION.
• ROW_COL_VALIDATION: checks headers and row/column values.
• SCHEMA_VALIDATION: accepted by CLI contract but not implemented yet (run exits with a clear message).
• Combined mode: pass both as comma-separated values:
  • ROWCOUNT_VALIDATION,ROW_COL_VALIDATION

Key Features in Current Implementation

• Header mismatch detection:
  • header length mismatches
  • header name mismatches
• Row alignment using preferred key columns:
  • employee_id, id, emp_id, record_id, pk
  • falls back to first column if no preferred key exists
• Mismatch classification:
  • CELL
  • SRC_ONLY
  • TGT_ONLY
  • HEADER_LENGTH
  • HEADER_NAME
  • ROWCOUNT
• HTML report KPIs:
  • SRC Count
  • TGT Count
  • PASSED
  • FAILED
  • Pass Rate
  • Failed Rate
  • SRC Only
  • TGT Only
• Per-column filter inputs in mismatch table for quick triage.

Requirements

• Python 3.9+
• Packages:
  • openpyxl
  • pytest (for tests)
  • python-dotenv

Install dependencies:

pip install -r requirements.txt

Quick Start (Windows Batch Flow)

From project root:

scripts\001_env.bat
scripts\002_activate.bat
scripts\003_setup.bat

Run all mutation scenarios:

scripts\004_run.bat

Run a DVG validation and generate HTML:

scripts\dvg.bat

Run sheet mapping validation (Excel to Excel):

scripts\006_run_sheet_mapping.bat

Deactivate venv:

scripts\008_deactivate.bat

CLI Usage

DVG Validator

python dvg.py \
  --src-kind csv \
  --tgt-kind csv \
  --src-path inputs/employees.csv \
  --tgt-path outputs/employees.csv \
  --validation-type ROWCOUNT_VALIDATION,ROW_COL_VALIDATION \
  --html-output output/report_<datetime>.html

Legacy compatibility mode is still available:

python dvg.py \
  --file-type EXCEL \
  --src-path inputs/employees.csv \
  --tgt-path outputs/employees.csv \
  --validation-type ROWCOUNT,ROW_COL_VALIDATION

Optional arguments:

• --src-sheet <sheet_name>
• --tgt-sheet <sheet_name>
• --sheet-mapping "SRC1:TGT1,SRC2:TGT2"
• --chunk-size <positive_int> (default: 1000)
• --src-db-alias <alias>, --tgt-db-alias <alias>
• --src-env <env>, --tgt-env <env>, --allow-cross-env
• --max-mismatches <int>
• --key-mode <AUTO|PRIMARY_KEY|COLUMNS|GROUP_CANONICAL|HASH>

Notes:

• --sheet-mapping is supported only for Excel file pairs.
• Provide either --file-type or both --src-kind and --tgt-kind.
• --file-type remains supported for backward compatibility.
• DB kind declarations include sqlserver and oracle, but current implementation supports DB execution only for sqlite, postgresql, and mysql.
• Mixed file<->DB validation in a single run is not implemented yet.
• <datetime> token in --html-output is replaced at runtime with YYYYMMDD_HHMMSS.
• --chunk-size controls the number of data rows read per batch for CSV/XLSX loading.
• --max-mismatches truncates mismatch details included in console preview and HTML report.
• Console output now shows chunk progress for source/target loading: total chunks, current chunk, and completion summary.

Large-file tuning tip:

• Start with --chunk-size 1000 (default), then increase to 2000 or 5000 for faster reads if memory allows.
• In dvg.bat, set CHUNK_SIZE in the config block to tune batch size without changing CLI commands.

Installed CLI Entry Point

If installed as a package, you can run:

dvg --src-kind csv --tgt-kind csv --src-path ... --tgt-path ... --validation-type ROWCOUNT_VALIDATION

Data Mutation Utility (data_corruptor.py)

Use this utility to generate controlled data drift before validation.

Example:

python data_corruptor.py \
  --input inputs/employees.csv \
  --output outputs/employees_typos.csv \
  --column email \
  --percentage 1.0 \
  --type typo

Batch Scripts for Mutation Scenarios

Located in the scripts/ folder:

• run_case_swap.bat - Swap character cases
• run_date_shift.bat - Shift dates by random days
• run_nullify.bat - Replace values with NULL/empty
• run_numeric_shift.bat - Shift numeric values
• run_typo.bat - Introduce character typos

Example:

scripts\run_case_swap.bat

Supported mutation types:

• nullify
  • Replaces selected values with blank strings.
  • Purpose: validate missing-value detection.
• case_swap
  • Swaps letter casing in selected values.
  • Purpose: validate case sensitivity behavior.
• numeric_shift
  • Adds/subtracts a numeric offset (--value).
  • Purpose: validate precision and tolerance checks.
• date_shift
  • Shifts date/datetime values by day count (--value).
  • Supported formats: YYYY-MM-DD, YYYY-MM-DD HH:MM:SS.
  • Purpose: validate temporal drift handling.
• typo
  • Randomly replaces one character in selected strings.
  • Purpose: validate strict text/hash mismatch detection.

Sample Scenario Scripts

• run_case_swap.bat
• run_date_shift.bat
• run_nullify.bat
• run_numeric_shift.bat
• run_typo.bat

Each script mutates inputs/employees.csv into a corresponding file under outputs/.

Reports

Generated reports are written under output/ and include:

• high-level pass/fail status
• validation metadata (source, target, validation type, timestamp)
• KPI cards
• detailed mismatch table with filters

Tests

Run tests with:

pytest

Local Test Scripts

Windows:

scripts\005_run_unit_tests.bat
scripts\005_run_code_cov.bat

Linux/macOS:

bash scripts/005_run_unit_tests.sh
bash scripts/005_run_code_cov.sh

Coverage command used by the scripts:

python -m pytest --cov=data_validation_gini --cov-report=term-missing --cov-report=html

Current target and baseline: 100% coverage for package modules under src/data_validation_gini.

Security Audits

The project includes comprehensive security scanning with automated HTML report generation. See docs/security/SECURITY_AUDITS.md for detailed documentation.

Quick Start

Run all security audits:

scripts\013_run_all_security_audits.bat

Or on Linux/macOS:

bash scripts/013_run_all_security_audits.sh

Individual audit scripts:

• scripts/010_run_pip_audit.bat - Scan Python dependencies for known vulnerabilities
• scripts/011_run_trivy_audit.bat - Scan filesystem for misconfigurations and secrets
• scripts/012_run_gitleaks_audit.bat - Detect accidentally committed secrets

Reports Generated:

• audits/pip_audit_report.html - Dependency vulnerability report
• audits/trivy_fs_report.html - Filesystem audit report
• audits/gitleaks_report.html - Secret detection report

Install Security Tools:

# Windows (Chocolatey)
choco install trivy gitleaks
pip install pip-audit

# macOS (Homebrew)
brew install trivy gitleaks
pip install pip-audit

See docs/security/SECURITY_AUDITS.md for:

• Detailed tool documentation
• CI/CD integration examples
• Troubleshooting guides
• Report interpretation tips

Project Structure (High Level)

Core Files

• src/data_validation_gini/dvg.py - validation CLI implementation
• src/data_validation_gini/dvg_report.py - HTML report generation
• src/data_validation_gini/data_corruptor.py - mutation utility implementation
• src/data_validation_gini/dvg_db.py - database connectivity and table loading
• src/data_validation_gini/file_stores.py - INI/JSON file reader-writer classes
• dvg.py, dvg_db.py, dvg_report.py, data_corruptor.py - root compatibility wrappers
• README.md - Main documentation
• docs/CONTRIBUTING.md - contributor workflow and repository boundaries
• docs/security/SECURITY_AUDITS.md - Security audit scripts documentation

Scripts Folder (scripts/)

Setup & Environment:

• 001_env.bat/sh - Python environment setup
• 002_activate.bat/sh - Activate virtual environment
• 003_setup.bat/sh - Install dependencies
• 008_deactivate.bat/sh - Deactivate virtual environment

Domain Implementations:

• scripts/data/ - operational data workflows (mutations, sheet mapping, DB startup/seed/compare)
• scripts/testing/ - local test and coverage workflows
• scripts/security/ - security audit workflows and consolidated run

Compatibility Wrappers (root scripts):

• Existing root scripts remain valid (for example 004_run.bat, 005_run_unit_tests.bat, 010_run_pip_audit.bat).
• Each wrapper forwards to the new domain script path so existing entrypoints and automation remain unchanged.

Validation & CLI:

• dvg.bat/sh - Run DVG validation

Directories

• inputs/ - baseline sample datasets
• outputs/ - mutated sample datasets
• output/ - generated validation report files
• audits/ - generated security audit reports (JSON & HTML)
• tests/ - unit tests
• data_validation_gini.egg-info/ - package metadata

License

MIT