Fact Tables: Core business entities containing primary data for series, schools, institutions, and careers
Geographic Dimensions: Hierarchical location data enabling regional analysis and search
Educational Dimensions: Supporting reference data for academic structure and business context
Bridge Tables: Many-to-many relationships enabling complex cross-entity queries and analysis
Statistics Tables: Aggregated data for reporting, trends analysis, and performance metrics
Profile System: Career guidance through inspirational success stories and pathways

🔄 Key Database Relationships

Primary Fact-to-Dimension Relationships

Series → Filieres (Many-to-One) • Lycees → Departments → Regions (Hierarchy) • Institutions → Categories & Regions • Careers → Sectors • Companies → Sectors & Regions

Cross-Fact Relationships (Many-to-Many via Bridge Tables)

Series ↔ Lycees (via lycee_series) • Series ↔ Subjects (via series_subjects) • Series ↔ Formations (via formation_series_requirements) • Formations ↔ Careers (via formation_career_prospects) • Careers ↔ Companies (via career_companies)

Statistical & Analysis Relationships

Student_Statistics → Series, Filieres, Lycees, Geographic dimensions • Teaching_Statistics → Series, Filieres, Geographic dimensions • Reference_Profiles → Careers, Series, Companies, Institutions

Self-Referencing Relationships

Sectors → parent_sector_id (Hierarchical) • Careers → Similar careers (via career_similarities) • Subjects → Prerequisite subjects

⚡ Performance & Scalability Features

🗂️ Partitioning Strategy

Statistical tables partitioned by academic year for optimal query performance and easier archiving of historical data.

📇 Composite Indexing

Strategic composite indexes on frequently queried combinations like (department_id, lycee_type, status) for fast filtering.

🔍 Full-Text Search

FULLTEXT indexes on name and description fields enabling fast search across institutions, formations, and careers.

📊 Covering Indexes

Covering indexes that include frequently accessed columns to avoid table lookups and reduce I/O operations.

💾 Materialized Views

Pre-computed aggregations for complex statistical queries and dashboard metrics to ensure sub-second response times.

🔄 Read Replicas

Separate read replicas for heavy analytical workloads while maintaining write performance on the master database.

🚀 Implementation Roadmap

Phase 1: Foundation (Weeks 1-4)

Geographic dimensions • Educational structure (filieres, subjects) • Basic lycees and series data • Core relationships

Phase 2: Higher Education (Weeks 5-8)

Institution categories • Higher education institutions • Formations and requirements • Series-formation relationships

Phase 3: Career System (Weeks 9-12)

Sectors and companies • Careers with detailed attributes • Career-formation mappings • Company-career relationships

Phase 4: Analytics & Statistics (Weeks 13-16)

Statistical data tables • Performance metrics • Dashboard queries • Reporting optimization

Phase 5: Profiles & Information (Weeks 17-20)

Reference profiles system • Career journeys • FAQ system • Advanced search features

Phase 6: Advanced Features (Weeks 21-24)

Career similarity algorithms • Recommendation engine • Mobile optimization • Advanced analytics

🛠️ Technical Specifications

📏 Scale Estimates

Series: ~50 records
Lycees: ~2,000 records
Institutions: ~500 records
Formations: ~5,000 records
Careers: ~1,000 records

💽 Storage Planning

Core Data: ~500MB
Statistics: ~2GB/year
Indexes: ~1GB
Total (5 years): ~12GB

🎯 Query Performance

Simple searches: <50ms
Complex filters: <200ms
Statistical reports: <1s
Full-text search: <100ms

🔧 Technology Stack

Database: MySQL 8.0+
Search: Elasticsearch
Cache: Redis
Analytics: ClickHouse

✅ Data Quality & Governance

Data Validation Rules

Referential Integrity: All foreign key constraints enforced • Business Rules: Custom triggers for data validation • Audit Trail: created_at/updated_at timestamps on all tables

Data Sources & Updates

Official Sources: Ministry of Education, ANAQ-SUP • Update Frequency: Statistical data annually, core data as needed • Quality Scoring: Reliability scores for all statistical data

Backup & Recovery

Daily Backups: Full database backup with point-in-time recovery • Archive Strategy: Historical data moved to archive tables after 5 years • Disaster Recovery: Cross-region replication for business continuity