Performance Optimization and Scalability

Efficient caching strategies are critical for improving application performance and reducing database load. This module introduces the concept of caching and its significance in optimizing .NET Core applications. Learners will explore in-memory caching and distributed caching implementations, understand cache expiration and invalidation strategies, and identify best practices for integrating caching mechanisms. By the end of this module, participants will be able to leverage caching effectively to enhance application responsiveness and scalability.

Database performance is a crucial factor in ensuring efficient application operation. This module covers query optimization techniques that improve database responsiveness. Learners will explore various indexing strategies, query performance analysis tools, and optimization techniques for handling complex queries. Additionally, best practices for maintaining efficient database queries will be discussed. By mastering these techniques, developers can enhance the performance of their applications and minimize resource consumption.

Building applications that can handle increasing loads without performance degradation is a key aspect of back-end development. This module focuses on the principles and architectural patterns that support scalability, including load balancing, traffic management, and asynchronous processing. Learners will gain insights into designing systems that effectively scale to meet growing user demands. By the end of this module, participants will be equipped with best practices for creating robust and scalable applications.

AI-powered tools like Microsoft Copilot can significantly enhance performance optimization in software development. This module provides hands-on experience in using Microsoft Copilot to analyze and improve application code and database performance. Learners will explore how Copilot assists in identifying performance bottlenecks, optimizing database queries, and implementing comprehensive performance enhancements. By leveraging AI-driven insights, developers can streamline optimization processes and improve application efficiency.