Data Modeling and Lakehouse Architecture with SQL

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

• Design star and snowflake schemas using dimensional modeling principles
• Implement data warehouse architectures with partitioning and workload isolation
• Build lakehouse pipelines integrating file storage with structured querying
• Create semantic metrics layers and slowly changing dimension logic

• Prior hands-on experience with core concepts
• Ability to apply main tools independently