Cloud Data Architecture

Build Scalable, AI-Ready Data Platforms Cloud Data Architecture is the strategic design of data systems in the cloud that transform raw data into meaningful, actionable insights.

This course provides a comprehensive foundation in building modern, cloud-native data platforms using scalable, secure, and high-performance architectures.

You’ll learn how to leverage managed cloud services, decouple compute and storage, and implement elastic scaling to support today’s data-driven and AI-powered business environments.

As organizations accelerate digital transformation, cloud data architecture has become essential for enabling real-time analytics, machine learning, and AI innovation.

Traditional on-premises systems can’t keep pace with the volume, variety, and velocity of modern data.

This course explores how cloud-based solutions—such as data lakes, data warehouses, and lakehouse architectures—unlock agility, cost-efficiency, and advanced analytics capabilities.

You’ll also dive into emerging trends like semi-structured and unstructured data processing, vector embeddings, retrieval-augmented generation (RAG), and infrastructure optimized for large language models (LLMs) and intelligent agents—key components of next-generation data ecosystems.

Course Highlights: Foundations of Cloud Data Architecture: Cloud-Native Platforms Architectural Evolution of Cloud Platforms: Virtualization, Containers, Microservices, and Managed Services Data Platform Architectures Architectural Patterns for Modern Data Systems: Layered Architectures, Medallion Design, and Domain-Oriented Data Organization Compute and Storage Architecture: Elasticity, Workload Isolation, Performance, and Cost Optimization Data Modeling for Analytics: Relational, Dimensional, and Hybrid Approaches in Cloud Platforms Semantic Views and Semantic Layers: Reusable Metrics, Dimensions, and Governed Access for Reporting and Self-Service Analytics Architectures for Semi-Structured Data Architectures for Unstructured and Multi-Modal Data: Documents, Images, Staged Files, and External Object Storage Course Learning Outcomes: Explain the core principles of cloud-native data architecture, including virtualization, managed services, separation of compute and storage, elasticity, workload isolation, and consumption-based pricing Evaluate the roles of data lakes, data warehouses, lakehouses, and the Data Cloud in modern analytics ecosystems Design a layered cloud data architecture using Snowflake and AWS S3 to support ingestion, storage, duration, governance, and analytics consumption Design semantic views and governed reporting layers that support reusable metrics, dimensions, and self-service analytics Assess architectural options for work flow automation, security, access control, and secure data sharing in enterprise cloud data platforms Explain how modern cloud data architectures support AI use cases through vector embeddings, RAG patterns, Cortex AI services, and LLM-oriented data design Course Typically Offered: Online in Fall and Spring quarters.

Prerequisites: Basic proficiency in SQL, including SELECT, JOIN, and GROUP BY, and familiarity with relational database concepts.

Prior exposure to cloud computing concepts is helpful but not required.

Next Step: After completing this course, consider taking other courses in our Database Management Certificate to continue learning.

More Information: For more information about this course, please email unex-techdata@ucsd.edu .