Engineering Enterprise Cloud Solutions for Data-Intensive Applications: Optimizing Performance, Scalability, and Cost
Keywords:
cloud computing, data-intensive applications, performance optimizationAbstract
Engineering Enterprise Cloud Solutions for Data-Intensive Applications: Optimizing Performance, Scalability, and Cost
Big data analytics, artificial intelligence, and IoT are altering data-intensive applications that need for secure, scalable, reasonably priced commercial cloud solutions to examine vast volumes of data. Engineering is required in designing quick, scalable, reasonably priced data-intensive cloud systems. Control massive data, complicated processing, and real-time analytics cloud problems carefully. Research is on hybrid and multi-cloud architectures for data-intensive corporate applications.
We need faster data transmission, higher throughput, and reduced network latency. System efficiency may be limited by slow storage, retrieval, and processing depending on data-intensity. Review data sharding, caching, and computing resource allocation for low latency and data accessibility. In data-intensive applications, HPC and GPU handle difficult machine learning and deep learning chores. Containerizing and serverless computing might improve data resource agility.
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