Concepts Overview
Welcome to the Concepts section — a space dedicated to the foundational ideas that support effective analytics, visualization, and engineering work.
Understanding the tools is important — but understanding the concepts behind them is what turns dashboards into insights and pipelines into long-term solutions. This section provides the theory, frameworks, and mental models that underpin good data practice.
What You’ll Find Here:
Core principles of data modeling (star schema, normalization, relationships)
Metrics layers & semantic models — how to define and centralize business logic
ETL vs. ELT — understanding the transformation lifecycle
Modularity, reusability, and scalability in analytics workflows
Data quality, testing, and documentation as first-class citizens in your stack
Why Concepts Matter
Whether you’re building a Power BI report or a dbt model, the effectiveness of your work depends on how well you structure, interpret, and communicate data. These pages will help you think more critically and strategically — beyond tool features — about:
What questions you're answering
How data is being transformed
Who you're building for
Why things are modeled a certain way