Architect and Optimize GenAI Data Systems

Overview

The explosive growth of generative AI has created unprecedented demands on enterprise data infrastructure. Organizations struggle with complex data quality issues, escalating storage costs, and fragmented processing platforms that can't keep pace with AI workloads. This Short Course was created to help machine learning and AI professionals architect robust, cost-effective data systems that power reliable GenAI operations. By completing this course, you'll be able to trace data lineage to pinpoint quality issues affecting AI model performance, design storage tiers that balance access speed with budget constraints, and integrate streaming and batch platforms into unified architectures that scale with AI demands. By the end of this course, you will be able to: • Analyze lineage metadata to systematically diagnose root causes of data quality problems • Evaluate storage tiering strategies that optimize cost, latency, and throughput trade-offs • Create technical blueprints integrating Kafka, Spark, and Flink for scalable data processing This course is unique because it addresses the specific data architecture challenges that emerge when running AI systems at enterprise scale, combining cost optimization with performance requirements that traditional data engineering courses don't cover.To be successful in this project, you should have a background in data engineering, cloud infrastructure, and basic understanding of streaming vs batch processing patterns.

Syllabus

Module 1: Root Cause Analysis & Data Lineage

By the end of this module, learners will master systematic data quality troubleshooting by understanding lineage tracking, analyzing metadata graphs, and applying root cause analysis methodologies to diagnose issues affecting GenAI model performance in enterprise environments.

Module 2: Storage Optimization & Cost Analysis

By the end of this module, learners will master cost-effective storage architecture design by analyzing workload access patterns, evaluating tiering strategies across different storage technologies, and creating quantified optimization recommendations that balance performance requirements with budget constraints for enterprise GenAI systems.

Module 3: Platform Integration Blueprint

By the end of this module, learners will master unified data processing architecture design by analyzing platform integration patterns, creating technical blueprints that specify Kafka, Spark, and Flink interoperability, and developing Architecture Decision Records with deployment guidance for enterprise GenAI environments.