Open data formats modernize today's analytics ecosystem. This course will teach you how to design scalable and future-proof lakehouse architectures by using open table formats.

What you'll learn
Many organizations struggle to modernize their data platforms as traditional data lakes lack reliability, governance, and interoperability. Open table formats offer a path forward, but choosing and designing with them can feel overwhelming for teams new to the lakehouse paradigm. In this course, Design Lakehouse Architectures with Open Table Formats, you'll gain the ability to design scalable, governed, and future-proof lakehouse architectures using formats such as Delta Lake, Apache Iceberg, and Apache Hudi.
First, you'll explore how open table formats support effective data modeling by enabling bronze/silver/gold patterns, improving data quality workflows, and supporting governance and lineage across the lakehouse. Next, you'll discover how table format capabilities influence architectural decisions for BI, analytics, and machine-learning workloads. Finally, you'll learn how to evaluate, compare, and select the right table format based on workload characteristics, ecosystem support, migration paths, and organizational requirements—building a repeatable decision framework you can apply in real projects. When you're finished with this course, you'll have the skills and knowledge needed for lakehouse design with open table formats.