# Future Development Plan

This document outlines the planned features and roadmap for Schema Sentinel. It includes both near-term improvements and long-term strategic initiatives.

## Overview

Schema Sentinel is continuously evolving to meet the needs of data engineers and database administrators. This roadmap reflects community feedback, technical requirements, and strategic vision for the project.

## Current Status

**Version**: 2.1.0  
**Status**: Active Development 🚧

### Recently Completed

- ✅ Modern Python packaging with `pyproject.toml`
- ✅ Development environment setup with `uv` package manager
- ✅ Comprehensive documentation and wiki
- ✅ GitHub Actions CI/CD workflows
- ✅ Pre-commit hooks and code quality tools
- ✅ Basic test suite with pytest
- ✅ Snowflake metadata extraction
- ✅ Markdown and HTML report generation

## Near-Term Goals (v2.2.0 - Q2 2026)

### 1. DuckDB Integration for Enhanced Comparisons

**Goal**: Integrate DuckDB to generalize the core schema and document comparator functionality.

**Rationale**: DuckDB provides a powerful embedded analytical database engine that can significantly enhance comparison capabilities:
- Fast in-process analytical queries
- Better performance for complex comparisons
- Advanced SQL capabilities for comparison logic
- Efficient handling of large metadata sets
- No external database dependencies

**Implementation Plan**:

1. **Core Integration**
   - Add DuckDB as a dependency
   - Create DuckDB engine wrapper in `engine.py`
   - Implement metadata storage in DuckDB format
   - Develop schema comparison queries using DuckDB SQL

2. **Comparison Engine**
   - Refactor comparison logic to leverage DuckDB
   - Implement advanced diff algorithms using DuckDB queries
   - Enable complex join-based comparisons
   - Support historical comparisons across multiple snapshots

3. **Performance Optimization**
   - Benchmark DuckDB vs current SQLite approach
   - Optimize query patterns for large schemas
   - Implement efficient indexing strategies
   - Add parallel processing for multi-schema comparisons

**Benefits**:
- ⚡ Faster comparisons for large databases
- 🔍 More sophisticated comparison queries
- 📊 Better analytical capabilities
- 🚀 Improved scalability

**Status**: 🚧 Planning Phase

---

### 2. Generalized and Parametrized Data Comparator

**Goal**: Create a flexible, rule-based data comparison framework that goes beyond metadata to compare actual table data.

**Rationale**: Users need to compare not just schema but also data content between environments, with customizable comparison rules.

**Implementation Plan**:

1. **Comparison Rules Engine**
   - Define comparison rule schema (JSON/YAML)
   - Implement rule parser and validator
   - Support custom comparison functions
   - Enable rule inheritance and composition

2. **Comparison Types**
   - **Exact Match**: Row-by-row exact comparison
   - **Fuzzy Match**: Approximate matching with tolerance
   - **Aggregate Comparison**: Compare statistics (count, sum, etc.)
   - **Sample Comparison**: Compare random samples
   - **Hash Comparison**: Compare data hashes for large tables

3. **Parametrization**
   - Configurable comparison thresholds
   - Column-level comparison rules
   - Data type-specific comparisons
   - Nullable field handling
   - Timestamp tolerance settings

4. **Performance Considerations**
   - Streaming comparison for large datasets
   - Parallel processing by partition
   - Incremental comparison support
   - Memory-efficient algorithms

**Example Configuration**:

```yaml
comparison_rules:
  tables:
    - name: "customers"
      compare_mode: "exact"
      key_columns: ["customer_id"]
      exclude_columns: ["updated_at", "last_sync"]
      
    - name: "transactions"
      compare_mode: "aggregate"
      aggregations:
        - column: "amount"
          function: "sum"
          tolerance: 0.01
        - column: "transaction_id"
          function: "count"
          
    - name: "logs"
      compare_mode: "sample"
      sample_size: 10000
      sample_method: "random"

  global_rules:
    timestamp_tolerance: "1s"
    float_precision: 2
    null_equals_null: true
    case_sensitive: false
```

**Status**: 🚧 Design Phase

---

### 3. Additional Database Engine Support via Extensions

**Goal**: Implement a plugin architecture to easily add support for new database engines.

**Rationale**: Users work with diverse database technologies beyond Snowflake. A plugin system enables community contributions and rapid support for new databases.

**Planned Database Support**:

#### Priority 1 (v2.2.0)
- **PostgreSQL** 🐘
  - Full metadata extraction
  - Schema comparison
  - Function and procedure support
  - Extension metadata

- **MySQL/MariaDB** 🐬
  - Table and column metadata
  - Index and constraint extraction
  - Stored procedure support

#### Priority 2 (v2.3.0)
- **Oracle Database** 🔴
  - Schema and tablespace metadata
  - Package and procedure extraction
  - Advanced constraint types

- **Microsoft SQL Server** 🪟
  - Database and schema metadata
  - Function and procedure support
  - SQL Server-specific objects

- **DuckDB** 🦆 (native mode)
  - Local database support
  - Extension metadata
  - View and macro extraction

#### Priority 3 (v3.0.0)
- **BigQuery** ☁️
- **Redshift** 🔴
- **Databricks** 🧱
- **SQLite** (enhanced support)

**Extension Architecture**:

```python
# Plugin interface
class DatabaseExtension(ABC):
    """Base class for database extensions."""
    
    @abstractmethod
    def connect(self, config: dict) -> Connection:
        """Establish database connection."""
        pass
    
    @abstractmethod
    def extract_metadata(self, connection: Connection) -> MetadataContainer:
        """Extract metadata from database."""
        pass
    
    @abstractmethod
    def supported_objects(self) -> List[str]:
        """Return list of supported object types."""
        pass

# Plugin registration
@register_extension("postgresql")
class PostgreSQLExtension(DatabaseExtension):
    """PostgreSQL database extension."""
    
    def connect(self, config: dict) -> Connection:
        # PostgreSQL-specific connection logic
        pass
    
    def extract_metadata(self, connection: Connection) -> MetadataContainer:
        # PostgreSQL-specific extraction logic
        pass
```

**Extension System Features**:
- 🔌 Plugin discovery and registration
- 📦 Separate package distribution
- 🔧 Configuration validation
- 📚 Extension documentation
- ✅ Extension testing framework

**Status**: 🚧 Architecture Design

---

## Mid-Term Goals (v2.3.0 - Q3 2026)

### 4. Enhanced CLI Interface

- 📟 Interactive CLI with rich terminal output
- 🎨 Colored diff output
- 📊 Progress bars for long operations
- 💾 Configuration file support (.toml, .yaml)
- 🔄 Batch operations support

### 5. REST API

- 🌐 FastAPI-based REST API
- 📡 Metadata extraction endpoints
- 🔍 Comparison API
- 📝 Report generation API
- 🔐 Authentication and authorization
- 📖 OpenAPI/Swagger documentation

### 6. Advanced Reporting

- 📊 Enhanced HTML reports with interactive elements
- 📈 Charts and visualizations
- 📄 PDF export capability
- 📧 Email report delivery
- 🔔 Slack/Teams notifications
- 🎨 Customizable report templates

### 7. Historical Tracking

- 📅 Timeline view of schema changes
- 🔄 Version comparison across time
- 📊 Change frequency analysis
- 🔍 Object history tracking
- 📈 Schema evolution visualization

---

## Long-Term Goals (v3.0.0 - Q4 2026)

### 8. Web UI

- 🖥️ Modern React-based web interface
- 📊 Interactive comparison viewer
- 🔍 Schema explorer
- 📈 Dashboard with metrics
- 👥 Multi-user support
- 🔐 Role-based access control
- 📱 Mobile-responsive design

### 9. Data Quality Checks

- ✅ Built-in data quality rules
- 🔍 Anomaly detection
- 📊 Data profiling
- 📉 Statistical analysis
- 🚨 Quality alerts and notifications
- 📝 Quality report generation

### 10. CI/CD Integration

- 🔄 GitHub Actions integration
- 🚀 GitLab CI/CD support
- 📊 Schema validation in pipelines
- 🚦 Automated deployment checks
- 📝 Automatic documentation generation

### 11. Advanced Features

- 🤖 AI-powered change suggestions
- 🔮 Schema migration recommendations
- 📊 Impact analysis for changes
- 🔄 Automated synchronization
- 🏗️ DDL generation and execution
- 📈 Performance impact predictions

---

## Research & Exploration

### Areas Under Investigation

1. **Machine Learning Integration**
   - Pattern recognition in schema changes
   - Anomaly detection in metadata
   - Predictive analytics for schema evolution

2. **Graph Database Support**
   - Neo4j metadata extraction
   - Relationship visualization
   - Graph-based comparisons

3. **Real-time Monitoring**
   - Continuous schema monitoring
   - Change detection webhooks
   - Real-time alerts

4. **Cloud-Native Features**
   - Kubernetes deployment
   - Serverless execution
   - Multi-tenant architecture

---

## Version Roadmap

### v2.2.0 (Q2 2026) - Enhanced Comparison
- ✨ DuckDB integration
- ✨ Parametrized data comparator
- ✨ PostgreSQL support
- ✨ MySQL support
- 🐛 Bug fixes and improvements

### v2.3.0 (Q3 2026) - API & Integration
- ✨ REST API
- ✨ Enhanced CLI
- ✨ Advanced reporting
- ✨ Oracle support
- ✨ SQL Server support

### v3.0.0 (Q4 2026) - Web UI & Enterprise
- ✨ Web UI
- ✨ Multi-user support
- ✨ RBAC
- ✨ Historical tracking
- ✨ CI/CD integration
- ✨ Additional database engines

---

## Community Involvement

We welcome community contributions! Here's how you can help:

### High Priority Contributions Needed

1. **Database Extensions**
   - PostgreSQL engine implementation
   - MySQL engine implementation
   - Oracle engine implementation

2. **Testing**
   - Unit tests for new features
   - Integration tests
   - Performance benchmarks

3. **Documentation**
   - User guides
   - API documentation
   - Tutorial videos

4. **Feature Development**
   - DuckDB integration
   - Data comparator
   - Report templates

### How to Contribute

1. Check the [Contributing Guide](Contributing.md)
2. Look for issues labeled `good first issue` or `help wanted`
3. Propose new features via GitHub Discussions
4. Submit pull requests with tests and documentation

---

## Technical Debt & Improvements

### Code Quality
- [ ] Increase test coverage to >90%
- [ ] Add performance benchmarks
- [ ] Improve type hints coverage
- [ ] Refactor legacy code sections

### Infrastructure
- [ ] Enhanced CI/CD pipelines
- [ ] Automated release process
- [ ] Performance monitoring
- [ ] Error tracking integration

### Documentation
- [ ] Complete API documentation
- [ ] Video tutorials
- [ ] Example projects
- [ ] Architecture diagrams

---

## Feedback & Suggestions

We value your input! Share your ideas:

- 💡 **Feature Requests**: Open a GitHub issue with the `enhancement` label
- 💬 **Discussions**: Join [GitHub Discussions](https://github.com/Igladyshev/schema-sentinel/discussions)
- 🐛 **Bug Reports**: Open a GitHub issue with the `bug` label
- 📧 **Direct Contact**: Reach out to maintainers

---

## Stay Updated

- ⭐ **Star** the repository to stay informed
- 👁️ **Watch** for release notifications
- 📰 **Follow** project updates in Discussions
- 📖 **Read** CHANGELOG.md for version updates

---

**Last Updated**: February 4, 2026  
**Next Review**: May 2026

For more information, see:
- [Architecture](Architecture.md)
- [Development Guide](Development.md)
- [Contributing Guide](Contributing.md)
