Resilient Cloud Architectures for Optimized Big Data Storage and Real-Time Processing

The growing exponentially of data that is produced by modern-day applications and has established significant limitations of storage, process, and elasticity. Cloud-based systems have become a cheap and elastic response to Big data that has helped organizations manage huge data and process them effectively through distributed computing methods. The paper is devoted to some of the most important technologies, such as Hadoop Distributed File System (HDFS), Apache Spark, and cloud-native services, which are offered by such suppliers as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), and explores the design, implementation, and optimization of a cloud-based architecture of big data processing and storage. To solve security and compliance problems, the paper examines architectural models that can provide high availability, fault tolerance and scalability. Experimental evaluation demonstrated that integration of cloud elasticity and big data frameworks has significant effect on elevating the speed of processing, reducing operating cost, and enhancing decision making activities. The findings provide a checklist of sorts that companies seeking to leverage the cloud in performing large scale data analysis can come to as it indicates what would serve as the best practice when it comes to using cloud-based big data systems.