The goal of this course is to equip the participants to gain confidence in optimizing GoLang applications for better performance and scalability outcomes. Learners will learn about advanced profiling techniques, memory management tools, and strategies for scaling distributed systems. With the help of  real-world examples and scenarios covered in hands-on labs, participants will get practical insights into applying specific GoLang tools and methodologies while designing robust, high-performing applications. Finally, the course will focus on practical knowledge of profiling tools, cloud deployment strategies, and advanced network optimization.

By the end of this course, you will be able to analyze and optimize the performance of GoLang applications, starting with identifying the bottlenecks, and ending with implementing scalable solutions. You will get hands-on experience in using profiling tools like pprof and Delve, optimizing memory and CPU usage, and scaling applications in cloud environments.

Key Takeaways:

• Proficiency in performance profiling using pprof, GoTrace, and go-torch tools.
• Understanding of Go’s memory allocation and garbage collection tools.
• Ensure CPU efficiency with Goroutines and channels.
• Deploy scalable Go applications using Docker and Kubernetes.
• Improve network throughput using tcpdump and network libraries.
• Advanced optimization techniques for enhancing applications performance.