Dimensional, data vault, medallion, and semantic modeling approaches for enterprise data
Data modeling provides the structural foundation for how data is organized, stored, and accessed across analytics platforms. It translates business requirements into technical schemas that enable both performance and flexibility while maintaining data integrity and relationships.
This competency encompasses multiple modeling methodologies, each optimized for different use cases and analytical patterns, from traditional dimensional warehousing to modern lakehouse architectures.
Traditional data warehouse modeling approach optimized for analytical workloads and business intelligence reporting.
Central fact tables surrounded by dimension tables, providing intuitive business views with optimal query performance for aggregations and filtering.
Normalized dimensional structures that reduce storage requirements while maintaining analytical performance for complex hierarchical data.
Type 1, 2, and 3 SCD patterns for managing historical changes in dimensional attributes while preserving analytical consistency.
Shared dimension structures across multiple data marts enabling consistent cross-functional reporting and drill-across capabilities.
Agile, auditable modeling methodology that separates business keys, relationships, and descriptive attributes for maximum flexibility and historical tracking.
Core business entities containing unique business keys and minimal metadata, providing stable anchor points for data integration.
Relationship structures that capture associations between business entities with full auditability and temporal tracking.
Descriptive attributes and context data with complete change history, enabling time-variant analysis and compliance requirements.
Performance optimization structures that provide efficient access to historical state across multiple satellites for reporting and analytics.
Modern lakehouse modeling pattern that organizes data processing through bronze, silver, and gold layers for progressive refinement and consumption.
Unprocessed data in its original format providing an immutable source of truth with full lineage and audit capabilities.
Cleaned, validated, and standardized data with consistent schemas, data quality checks, and business rules applied.
Business-ready data optimized for specific use cases including aggregated metrics, KPIs, and domain-specific data marts.
ACID transaction support with time travel capabilities enabling both batch and streaming data processing patterns.
Business-focused data models that provide consistent definitions, metrics, and relationships for self-service analytics and reporting.
Centralized business logic layer that provides consistent metric definitions across all analytical tools and platforms.
Advanced analytical expressions for calculated columns, measures, and complex business logic in Power BI and Analysis Services.
Centralized repositories for business metrics with versioning, documentation, and governance to ensure analytical consistency.
User-friendly abstractions that enable business users to create reports and analyses without deep technical knowledge.
Contemporary modeling approaches that support real-time analytics, streaming data, and cloud-native architectures.
Capturing business events as immutable facts enabling temporal analysis and system state reconstruction at any point in time.
Modeling complex relationships and networks for fraud detection, recommendation systems, and social network analysis.
Designing schemas and processing patterns for continuous data streams enabling immediate insights and operational responses.