Data Modeling and Lakehouse Architecture with SQL

About this course

You will design and implement enterprise-grade data models, from traditional star schemas to modern lakehouse architectures. This comprehensive course equips you with the skills to build cost-effective, scalable data solutions that drive business intelligence and analytics. You'll gain hands-on experience creating dimensional models with surrogate keys, optimizing database schemas through partitioning and clustering, and implementing slowly changing dimensions for historical data tracking. The course covers advanced topics like semantic metrics layers, multi-cluster warehouse architectures, and open-source table formats for data lakes. What makes this course unique is its end-to-end approach to modern data architecture. You'll work with real-world scenarios, from analyzing storage costs to designing data ingestion pipelines that span from raw files to analytics-ready tables. By completion, you'll confidently architect data solutions that balance performance, cost, and scalability—skills essential for senior data engineering and architecture roles in today's data-driven organizations.

What you will learn

• Design star and snowflake schemas with dimensional modeling principles, creating fact and dimension tables optimized for analytics.
• Implement cost-effective data warehouse architectures with partitioning, clustering, and multi-cluster workload isolation strategies.
• Build lakehouse data pipelines that integrate file-based storage with structured querying using external tables and modern formats.
• Create semantic metrics layers and slowly changing dimension logic to ensure data consistency across enterprise analytics systems.