Data Modeling, Transformation, and Serving Foundations

Modern organizations run on data, but raw data is rarely ready for immediate analysis or machine learning. Understanding how to structure, clean, and deliver this data is the key to unlocking its true value. This text-based course guides you through the core principles of data architecture, helping you transition from raw data ingestion to structured, high-performance data serving. You will learn how to design robust data models and write efficient transformations that power both business intelligence dashboards and predictive machine learning workflows. What you'll learn: - Understand foundational data modeling concepts, including normalization, star schemas, Inmon versus Kimball methodologies, and One Big Table (OBT) designs. - Compare data structures optimized for traditional business analytics against those required for tabular machine learning datasets. - Transform raw datasets using dbt (data build tool) to build reliable, documentable, and testable data pipelines. - Apply modern data engineering practices, including semantic layers and version-controlled transformation workflows. - Configure data serving layers to deliver clean, structured data efficiently to downstream analytics tools and ML models. The course begins with core definitions and architectural theory before moving into practical transformation scenarios. You will read through step-by-step SQL and dbt patterns, exploring how to structure tables for optimal query performance and machine learning readiness. This course is designed for aspiring data engineers, data analysts, and database beginners who want to build a solid foundation in data structuring. No advanced programming or prior data modeling experience is required. Start building clean, reliable, and high-performing data pipelines today.