Business Rule Validator (BRV) Accelerator — One Framework, One Language, Trusted Data

**Overview**
Modern data teams waste time reinventing data-quality checks in every project. The Business Rule Validator (BRV) accelerator fixes that with a structured, framework-based approach that every team—developers, data quality analysts, and business analysts—can use across the organization. Instead of ad-hoc scripts and one-off standards, BRV gives you a common library of reusable rules that you can extend with simple SQL.

**What it is**
- A shared rules framework: Centrally define, version, and reuse business and data-quality rules.
- SQL-first extensibility: Add new validations by writing straightforward SQL—no complex code required.
- Runs where your data lives: With Exponam.Connect, teams can execute business rule validations and data-quality checks seamlessly on Databricks.
- Automation with PYBRV: PYBRV orchestrates rule execution, reconciliation, and reporting—so teams don’t need to write complex logic to ensure accuracy, consistency, and trust.

***Why data teams love it (by role):***

*Developers / Data Engineers*
- Standardize validations across pipelines without bespoke code.
- Trigger rules as part of CI/CD and jobs on Databricks.
- Reduce maintenance with versioned, reusable rule sets..

*Data Quality Analysts*
- Automate execution + reporting for hundreds of rules at scale.
- Perform source–target reconciliation to catch drift and mapping errors early.
- Track rule coverage, pass/fail trends, and data health with dashboards.

*Business Analysts / SMEs*
- Express business logic as readable SQL checks (e.g., revenue must be non-negative, policy status must match coverage dates).
- AI-assisted test-case recommendations to improve coverage.
- Self-serve interactive visualizations to investigate issues—no deep technical expertise required.

**Key Capabilities**

- Automated rule runs & reports (via Databircks Jobs & Dashboards)
- Databricks-native execution (via Databricks SQL Warehouse)
- Source↔Target reconciliation for migrations and pipelines
- AI-recommended test cases to expand coverage fast
- Interactive dashboards for pass/fail, exceptions, and trends
- Auditability & compliance with rule lineage, owners, and change history

**How it works (at a glance)**
- Author rules in SQL (using templates for common patterns like nulls, ranges, referential integrity, duplicates).
- Register rules in the BRV catalog with owners, criticality, and SLAs.
- Execute at scale on Databricks using Exponam.Connect; PYBRV schedules and orchestrates runs.
- Report to curated tables and dashboards; alert on failures and SLA breaches.
- Improve using AI suggestions, failure clustering, and rule performance analytics.

**Product Details**

Business logic is expressed in validation scripts and configuration in JSON files, allowing wider adoption across technical and business teams.

- Automated Daily Validations
- Configuration-Driven: Handles business rules setup using JSON files.
- Support categorisations and define the severity of rules.
- Immediate Results: Validation pipelines write results into metadata tables.
- Dashboards provide test insights with sample rejected records.
- Incremental validation tracking with Bookmark Dates
- Trend monitoring.
- Test Case Recommendations
- SQL-Based Business Rule Validations
- Parameterised SQLs
- Alerts and Notifications

**Enterprise Deployment**

- For large-scale rollouts, [Complere Infosystem](https://complereinfosystem.com/) offers enterprise-grade deployment support:
- Custom rule integration aligned with organisational governance models
- Advanced, domain-specific dashboards
- Continuous improvement of validation rules over time
- Scalable deployments across multi-cloud and hybrid environments

**Availability and Licensing**

- Package: Available as an open-source Python package on PyPI.
- License: Open-source (Apache 2.0).
- Enterprise Deployment: Enhanced dashboards, enterprise folder structures, and integration support are available for organisations requiring large-scale rollouts.
- Users may request a trial license & implementation support by contacting [e-mail] or [e-mail] or by visiting [here](https://complereinfosystem.com).

**How to Use PYBRV**

PYBRV provides a guided, notebook-driven workflow:

**1. Setup Metadata (One-time)**

- Run PYBRV-Setup notebook to initialise schemas, configs, and dashboards.

**2. Generate Test Case Recommendations**

- Run PYBRV-Recommendation notebook, providing a list of tables.
- Output: Suggested test cases generated by the AI engine.

**3. Validate Business Rules**

- Run PYBRV-Business Rule Manager notebook.
- Rules are sourced from business_rules.json (sample config file).
- User can modify the rules and SQL queries as per their requirements.
- Execution results are automatically logged in metadata tables.

**4. Perform Data Reconciliation**

- Run PYBRV-Data Parity Checks notebook.
- Compares source and target datasets by parity_checks.json(sample config file).
- User can modify the rules and sql queries as per their requirements.
- Detects mismatches at both row and attribute levels.
- Produces reconciliation summary tables.

**5. View Results in the Dashboard**

- Open the PYBRV Dashboard (deployed during setup).
- Monitor rule execution summaries.
- Review mismatch reports.
- Track historical validation runs.

**Watch how to setup PYBRV in simple steps**
https://youtu.be/tAKVHXNeXHM

**Installation**
Install the package: %pip install pybrv