Data Modeling & Semantics

Modular Dimensional Data Modeling offers a revolutionary approach to data engineering, empowering analytics professionals to build scalable, maintainable data models. By leveraging modular design principles and dimensional modeling techniques, this methodology enables streamlined data transformation, effortless integration of new data sources, and enhanced data accessibility for business intelligence and advanced analytics. For data-driven organizations seeking to unlock the full potential of their data, Modular Dimensional Data Modeling provides a strategic edge in navigating the complexities of modern data ecosystems.

Explores the concept of semantic layers in business intelligence (BI) tools, which provide a governed and reusable data model for dashboards and reports. Covers topics like LookML, Power BI datasets, dbt Metrics, and how to build universal definitions, join logic, and shared dimensions/measures that can be leveraged across multiple BI platforms.

This article explores the practical application of the 4 transformation layers (raw, staging, dimensional, and reporting) in a data warehouse architecture. It covers topics like fact/dimension modeling, materialized views, and pipeline orchestration to ensure structure and maintainability in your data infrastructure.