# ExDataCheck Implementation Roadmap

## Vision

Build a production-ready data validation and quality library for Elixir ML pipelines that rivals Python's Great Expectations in functionality while leveraging Elixir's strengths in concurrency, fault tolerance, and distributed systems.

## Phase 1: Core Validation Framework (v0.1.0) - Weeks 1-4

### Week 1: Foundation

**Goal:** Establish core data structures and basic validation flow

- [ ] Define core data structures
  - [ ] `Expectation` struct and behavior
  - [ ] `ExpectationResult` struct
  - [ ] `ValidationResult` struct
  - [ ] `ValidationContext` for execution state

- [ ] Implement basic validator engine
  - [ ] Batch validator
  - [ ] Expectation executor
  - [ ] Result aggregator
  - [ ] Error handling

- [ ] Column extraction utilities
  - [ ] Extract from list of maps
  - [ ] Extract from keyword lists
  - [ ] Handle missing columns gracefully

**Deliverables:**
- Working validator that can execute basic expectations
- Comprehensive test suite for core functionality
- Documentation for core APIs

### Week 2: Value Expectations

**Goal:** Implement all value-based expectations

- [ ] Basic value expectations
  - [ ] `expect_column_values_to_be_between/3`
  - [ ] `expect_column_values_to_be_in_set/2`
  - [ ] `expect_column_values_to_match_regex/2`
  - [ ] `expect_column_values_to_not_be_null/1`
  - [ ] `expect_column_values_to_be_unique/1`

- [ ] Advanced value expectations
  - [ ] `expect_column_values_to_be_increasing/1`
  - [ ] `expect_column_values_to_be_decreasing/1`
  - [ ] `expect_column_value_lengths_to_be_between/3` (for strings)

- [ ] Tests and documentation
  - [ ] Unit tests for each expectation
  - [ ] Property-based tests
  - [ ] Usage examples

**Deliverables:**
- Complete value expectation library
- 100% test coverage
- Documentation with examples

### Week 3: Schema Validation

**Goal:** Implement schema definition and validation

- [ ] Schema definition DSL
  - [ ] Schema struct
  - [ ] Type system (integer, float, string, boolean, list, map)
  - [ ] Constraint system (required, unique, min, max, format)
  - [ ] Nested schema support

- [ ] Schema validator
  - [ ] Type checking
  - [ ] Constraint validation
  - [ ] Error collection
  - [ ] Type coercion (optional)

- [ ] Schema utilities
  - [ ] Infer schema from data
  - [ ] Schema merging
  - [ ] Schema validation (validate the schema itself)

**Deliverables:**
- Complete schema validation system
- Schema inference from sample data
- Comprehensive tests

### Week 4: Basic Profiling

**Goal:** Implement data profiling capabilities

- [ ] Column profiler
  - [ ] Type inference
  - [ ] Missing value detection
  - [ ] Cardinality calculation
  - [ ] Basic statistics (min, max, mean, median)

- [ ] Dataset profiler
  - [ ] Row count
  - [ ] Column count
  - [ ] Memory size estimation
  - [ ] Overall quality score

- [ ] Profile output
  - [ ] Profile struct
  - [ ] JSON export
  - [ ] Markdown report

**Deliverables:**
- Working profiler
- Profile export formats
- Integration with validator

## Phase 2: Statistical & ML Features (v0.2.0) - Weeks 5-8

### Week 5: Statistical Expectations

**Goal:** Implement statistical validation

- [ ] Distribution statistics
  - [ ] `expect_column_mean_to_be_between/3`
  - [ ] `expect_column_median_to_be_between/3`
  - [ ] `expect_column_stdev_to_be_between/3`
  - [ ] `expect_column_quantile_to_be/3`

- [ ] Distribution tests
  - [ ] `expect_column_values_to_be_normal/2`
  - [ ] `expect_column_distribution_to_match/3`

- [ ] Statistical utilities
  - [ ] Mean, median, mode calculations
  - [ ] Standard deviation, variance
  - [ ] Quantile calculations
  - [ ] Distribution fitting

**Deliverables:**
- Complete statistical expectation library
- Statistical utility module
- Tests and documentation

### Week 6: ML-Specific Expectations

**Goal:** Implement ML validation features

- [ ] Feature validation
  - [ ] `expect_feature_distribution/3`
  - [ ] `expect_feature_correlation/3`
  - [ ] `expect_feature_importance_order/2`

- [ ] Label validation
  - [ ] `expect_label_balance/2`
  - [ ] `expect_label_cardinality/2`
  - [ ] `expect_no_label_leakage/3`

- [ ] Data split validation
  - [ ] `expect_stratified_split/3`
  - [ ] `expect_temporal_split/2`

**Deliverables:**
- ML-specific expectations
- Integration examples with popular ML libraries
- Documentation for ML use cases

### Week 7: Data Drift Detection

**Goal:** Implement drift detection

- [ ] Drift detection methods
  - [ ] Kolmogorov-Smirnov test
  - [ ] Chi-square test
  - [ ] Population Stability Index (PSI)
  - [ ] Kullback-Leibler divergence

- [ ] Drift API
  - [ ] `create_baseline/2`
  - [ ] `detect_drift/3`
  - [ ] `expect_no_data_drift/2`

- [ ] Drift reporting
  - [ ] Drift scores per column
  - [ ] Drift visualization data
  - [ ] Drift summary report

**Deliverables:**
- Complete drift detection system
- Multiple drift detection algorithms
- Drift reports and visualizations

### Week 8: Advanced Profiling

**Goal:** Enhance profiling capabilities

- [ ] Advanced statistics
  - [ ] Correlation matrix
  - [ ] Outlier detection (IQR, Z-score)
  - [ ] Distribution characterization
  - [ ] Skewness and kurtosis

- [ ] Sampling strategies
  - [ ] Random sampling
  - [ ] Stratified sampling
  - [ ] Reservoir sampling for streams

- [ ] Profile comparison
  - [ ] Compare two profiles
  - [ ] Detect profile drift
  - [ ] Profile diff report

**Deliverables:**
- Enhanced profiling
- Sampling for large datasets
- Profile comparison tools

## Phase 3: Production Features (v0.3.0) - Weeks 9-12

### Week 9: Streaming Support

**Goal:** Full streaming dataset support

- [ ] Stream validator
  - [ ] Chunk-based validation
  - [ ] Result merging across chunks
  - [ ] Memory-efficient processing

- [ ] Stream profiler
  - [ ] Incremental statistics
  - [ ] Reservoir sampling
  - [ ] Sliding window analysis

- [ ] Stream utilities
  - [ ] Stream chunking
  - [ ] Parallel stream processing
  - [ ] Backpressure handling

**Deliverables:**
- Stream-based validation and profiling
- Performance benchmarks
- Large dataset examples

### Week 10: Quality Monitoring

**Goal:** Implement quality monitoring system

- [ ] Quality metrics
  - [ ] Completeness score
  - [ ] Validity score
  - [ ] Consistency score
  - [ ] Timeliness score
  - [ ] Overall quality score

- [ ] Monitor system
  - [ ] Quality tracker
  - [ ] Threshold-based alerting
  - [ ] Metric storage interface
  - [ ] Trend analysis

- [ ] Integration
  - [ ] Telemetry integration
  - [ ] Metrics export (Prometheus, StatsD)
  - [ ] Logging integration

**Deliverables:**
- Quality monitoring system
- Alerting framework
- Observability integrations

### Week 11: Pipeline Integration

**Goal:** Easy integration into ML pipelines

- [ ] Pipeline DSL
  - [ ] `use ExDataCheck.Pipeline`
  - [ ] Validation checkpoints
  - [ ] Error handling strategies
  - [ ] Pipeline composition

- [ ] Broadway integration
  - [ ] Broadway processor
  - [ ] Batching support
  - [ ] Error handling

- [ ] Flow integration
  - [ ] Flow stages
  - [ ] Parallel validation
  - [ ] Result aggregation

**Deliverables:**
- Pipeline integration module
- Broadway and Flow support
- Integration examples

### Week 12: Reporting & Export

**Goal:** Comprehensive reporting

- [ ] Report generators
  - [ ] Markdown reports
  - [ ] HTML reports
  - [ ] JSON export
  - [ ] CSV export

- [ ] Report templates
  - [ ] Validation report
  - [ ] Profile report
  - [ ] Drift report
  - [ ] Quality report

- [ ] Visualization data
  - [ ] Distribution plots data
  - [ ] Correlation heatmap data
  - [ ] Drift charts data
  - [ ] Quality trends data

**Deliverables:**
- Complete reporting system
- Multiple export formats
- Template customization

## Phase 4: Enterprise & Advanced (v0.4.0) - Weeks 13-16

### Week 13: Custom Expectations Framework

**Goal:** Extensibility for custom validations

- [ ] Custom expectation API
  - [ ] Expectation behavior
  - [ ] Helper macros
  - [ ] Testing utilities

- [ ] Expectation composition
  - [ ] Combine expectations
  - [ ] Conditional expectations
  - [ ] Parameterized expectations

- [ ] Expectation library
  - [ ] Domain-specific expectations
  - [ ] Composable validators
  - [ ] Reusable patterns

**Deliverables:**
- Custom expectation framework
- Documentation and examples
- Example custom expectations

### Week 14: Expectation Suites & Versioning

**Goal:** Manage expectation suites over time

- [ ] Suite management
  - [ ] Suite definition
  - [ ] Suite composition
  - [ ] Suite versioning

- [ ] Version control
  - [ ] Expectation versioning
  - [ ] Schema versioning
  - [ ] Migration support

- [ ] Suite storage
  - [ ] File-based storage
  - [ ] Database storage
  - [ ] Version history

**Deliverables:**
- Suite management system
- Versioning support
- Migration tools

### Week 15: Multi-Dataset Validation

**Goal:** Validate relationships across datasets

- [ ] Multi-dataset expectations
  - [ ] Cross-dataset joins
  - [ ] Referential integrity
  - [ ] Foreign key constraints

- [ ] Dataset relationships
  - [ ] Define relationships
  - [ ] Validate relationships
  - [ ] Relationship profiling

- [ ] Coordinated validation
  - [ ] Validate multiple datasets
  - [ ] Dependency ordering
  - [ ] Transaction-like validation

**Deliverables:**
- Multi-dataset validation
- Relationship validation
- Coordinated validation examples

### Week 16: Performance & Polish

**Goal:** Optimize performance and polish for release

- [ ] Performance optimization
  - [ ] Benchmark suite
  - [ ] Profiling and optimization
  - [ ] Parallel execution tuning
  - [ ] Memory optimization

- [ ] Documentation
  - [ ] Complete API documentation
  - [ ] Tutorial series
  - [ ] Best practices guide
  - [ ] Migration guides

- [ ] Polish
  - [ ] Error message improvements
  - [ ] Logging refinement
  - [ ] Configuration system
  - [ ] Default behaviors

**Deliverables:**
- Performance benchmarks
- Complete documentation
- v0.4.0 release

## Future Phases (v0.5.0+)

### Advanced Analytics
- Anomaly detection
- Time series validation
- Graph data validation
- Spatial data validation

### Distributed Systems
- Distributed validation
- Cluster-wide profiling
- Shared baseline storage
- Distributed drift detection

### ML Integration
- Integration with Nx/Axon
- Auto-generate expectations from models
- Model input validation
- Feature store integration

### Enterprise Features
- Role-based access control
- Audit logging
- Compliance reporting
- SLA monitoring

### UI/Visualization
- Web-based UI for reports
- Interactive profiling
- Real-time dashboards
- Drift visualization

## Success Metrics

### Phase 1
- [ ] Core validation works for common use cases
- [ ] 90%+ test coverage
- [ ] Documentation covers all public APIs

### Phase 2
- [ ] ML-specific features validated with real ML pipelines
- [ ] Drift detection comparable to existing tools
- [ ] Performance benchmarks published

### Phase 3
- [ ] Production deployments in real systems
- [ ] Stream processing handles millions of records
- [ ] Monitoring integrates with common observability tools

### Phase 4
- [ ] Community adoption
- [ ] Third-party extensions created
- [ ] Published to Hex.pm
- [ ] Production-proven at scale

## Release Strategy

### v0.1.0 - Core (Week 4)
- Basic validation
- Schema validation
- Simple profiling

### v0.2.0 - ML Features (Week 8)
- Statistical expectations
- ML validations
- Drift detection
- Advanced profiling

### v0.3.0 - Production (Week 12)
- Streaming support
- Quality monitoring
- Pipeline integration
- Reporting

### v0.4.0 - Enterprise (Week 16)
- Custom expectations
- Suite management
- Multi-dataset validation
- Performance optimization

### v1.0.0 - Stable (Week 20+)
- Production-proven
- Complete documentation
- Backward compatibility guarantees
- Long-term support

## Contributing

This roadmap is a living document. Contributions, suggestions, and feedback are welcome!

See the main README for contribution guidelines.