Learn how to design, evaluate, and optimize modern data lakehouse architectures for scalable analytics and machine learning. This course is designed for data engineers, analytics engineers, aspiring data architects, and learners interested in reliable, cost-efficient data platforms.

You'll explore how lakehouse architecture combines flexible cloud storage with data warehouse reliability. Examine transaction logs, ACID transactions, metadata management, schema evolution, and real-time data ingestion. Compare Delta Lake, Apache Iceberg, and Apache Hudi for batch, streaming, and analytical workloads. Discover how query optimization, partitioning, compaction, caching, and concurrency improve lakehouse performance. Explore multi-cloud interoperability, data mesh, liquid clustering, orchestration, observability, access control, and lakehouse FinOps. You'll also evaluate medallion architecture file formats, benchmarking, disaster recovery, and incremental migration strategies. By the end of this course, you'll be able to assess lakehouse architectures, compare open table formats, evaluate performance and reliability strategies, and make informed storage, governance, and cost decisions. What makes this course distinctive is its integrated approach to lakehouse architecture, performance optimization, and enterprise governance, helping you evaluate scalable data platforms against real-world requirements.