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.