Lakehouse Architecture for AI-Native Data Platforms

О курсе

This course focuses on designing governed, scalable lakehouse architectures that support AI-native data platforms. Learners translate AI workload requirements into data product SLOs, compare open table formats, design ingestion and replay strategies, manage schema evolution, support reproducibility, and define observability signals for freshness, latency, throughput, and cost. The course emphasizes architecture and operational patterns rather than vendor-specific platform administration. By the end of the course, learners can explain when a lakehouse is preferable to a warehouse or data lake for AI workloads, compare Delta Lake, Iceberg, and Hudi at a practical level, design replay-safe ingestion patterns, and apply governance controls such as data contracts, lineage, access control, audit logging, and CI gates. The result is a production-oriented platform design for governed AI data systems.

Навыки, которые вы освоите

Учитесь у экспертов

Antonio Cangiano

Engineering Manager and AI Specialist

Ruslan Podgaets

Преподаватель курса