Data Platform
Summary
This track covers the data foundations required for enterprise AI systems. It focuses on how to organize data architecture, governance, and quality so models and copilots can run safely and reliably in production.
You will move from platform design patterns to practical AI data readiness topics, including lineage, multimodal data, streaming, and quality controls.
Learning Objectives
- Understand modern data platform patterns for AI workloads.
- Compare lakehouse and medallion approaches for enterprise analytics and AI.
- Design vector and multimodal data flows for search and generation scenarios.
- Apply catalog, lineage, and quality practices for trustworthy AI data.
Module Path
- Modern Data Platform
- Lakehouse Architecture
- Medallion Architecture
- Vector Databases
- Multimodal Data
- Streaming and Realtime
- Data Catalog and Lineage
- AI-Ready Data
- Data Quality for AI
Next Steps
- Start with Modern Data Platform to align on core architecture concepts.
- Continue to AI-Ready Data once baseline platform patterns are clear.