Performance Optimization & Scalability Engineering is a highly technical course focused on improving application responsiveness, throughput, and system capacity to handle increasing workloads. The course begins by defining performance metrics—latency, throughput, response time, and concurrency—and introduces scalability models such as vertical, horizontal, and elastic scaling. Learners explore performance bottlenecks using profiling tools like Chrome DevTools, JProfiler, and VisualVM for applications in JavaScript, Python, and Java. The course covers backend optimization techniques including database indexing, query optimization (SQL and NoSQL), connection pooling, and in-memory caching with Redis or Memcached. Frontend performance is addressed with techniques like lazy loading, bundle optimization, and asset minification. Network-level strategies such as content delivery networks (CDNs), HTTP/2, and compression protocols are discussed. Scalability design patterns like load balancing, sharding, asynchronous processing, and event-driven architecture are explained and implemented. The course also introduces queueing systems (e.g., Kafka, RabbitMQ), microservices orchestration, and distributed caching strategies. Learners practice applying autoscaling rules in cloud platforms like AWS, Azure, and GCP. Topics such as SLOs/SLAs, synthetic monitoring, and observability are covered using tools like Grafana, Prometheus, and New Relic. By the end of the course, learners will be equipped to design and optimize software systems capable of handling real-world traffic and scaling with growing user demand.