This module teaches learners to turn AI workload needs into measurable data product SLOs and use those SLOs to evaluate lakehouse, warehouse, and data lake architecture choices. Learners build project-ready requirements, tradeoff notes, and an initial architecture recommendation for AI-native and multimodal data products.

This module teaches learners how to compare Delta Lake, Apache Iceberg, and Apache Hudi for AI native lakehouse workloads, with emphasis on reliability, reproducibility, and governance. Learners design decision artifacts for table format selection, time travel and snapshot strategy, rollback readiness, and catalog integration to support final project architecture choices.

This module teaches learners how to manage schema evolution, retention, lifecycle states, and deletion workflows in governed Lakehouse environments that support AI workloads. Learners examine compatibility risks across structured, nested, derived, and vectorized data, then design project-ready policies and workflows that preserve reliability, auditability, reproducibility, and compliance readiness.

Learn how to design reliable Lakehouse ingestion architectures by choosing among batch, CDC, streaming-style, and event-driven patterns based on freshness, latency, cost, and AI workload needs. You will also plan replay, backfill, idempotency, late-data handling, and validation gates so data can be safely promoted to trusted AI-ready states.

This module teaches learners how to operate an AI-native lakehouse reliably by making concrete decisions about compute provisioning, orchestration, observability, alerting, cost SLOs, and incident response. By the end, learners produce an operations package including a dashboard specification, alert matrix, cost SLOs, and runbook draft for common lakehouse incidents.

This module brings together governance, security, auditability, and CI controls to finalize a governed Lakehouse architecture for AI workloads. Learners define data contracts and evidence, map lineage and access controls, design CI gates, and complete a portfolio-ready final project package with clear tradeoffs and operational readiness.

This closing module wraps up Course 4 by celebrating your progress, reflecting on the course’s major architectural themes, and previewing the transition to Course 5. It reinforces how Lakehouse architecture foundations prepare you for unstructured data engineering in AI-native platforms.

This Final Exam assesses your ability to apply lakehouse architecture concepts across the full course in realistic decision making contexts. You will evaluate tradeoffs, connect requirements to design and operating choices, and demonstrate defensible reasoning for both platform architecture and governance.