Optimizing Database Performance in Large Projects

With the growing volume of data and increasing complexity of information systems, database optimization is becoming a key factor in the success of large projects. High database performance directly impacts query processing speed, application load times, and ultimately user satisfaction. This article examines modern methods and practices that help maximize database efficiency in large projects. The article focuses on aspects such as data structuring and normalization, indexing, sharding and replication. Various approaches to query optimization and transaction management are considered. It also covers scaling and load balancing, as well as using caching to reduce load on the underlying database. Reallife examples are provided to demonstrate the application of these methods in large projects. Another important aspect is the selection and configuration of a suitable caching mechanism, which helps reduce the load on the database. Considering horizontal scaling and data replication also plays an important role in maintaining high availability and performance in large projects. The purpose of the work is to consider the possibilities of optimizing database performance in large projects. Scientific works served as the theoretical basis