TOWARDS EFFICIENT BIG DATA ANALYTICS IN REGIONAL SYSTEMS: PRACTICAL INSIGHTS FROM HYBRID ARCHITECTURE DEPLOYMENT

Abstract

Conventional data processing architectures sometimes fall short of the needs of constantly growing data quantities in the quickly developing field of big data analytics. This study offers a thorough analysis of two well-known big data platforms, such as Hadoop and Spark, along with a brand-new hybrid architecture that makes use of both platforms' advantages. Conventional systems such as Hadoop offer strong storage capacities but are frequently constrained by slower processing speeds; on the other hand, Spark excels at processing data quickly but may encounter memory issues. Our work presents and puts into practice a Hybrid system that combines the fast-processing capabilities of Spark with the scalable storage of Hadoop to provide a solution that maximizes both speed and storage. This study assesses each system's efficiency, scalability, and performance using theoretical insights and real-world implementations across several datasets and various areas, such as the akimats of Almaty, Shymkent, and Turkestan. It is demonstrated that the hybrid design outperforms its competitors when performing intricate, large-scale data analysis jobs, offering notable gains in scalability, fault tolerance, and processing speed. This study shows how hybrid systems can be used to get around the drawbacks of the big data architectures that are in place now. It also offers a scalable, effective, and adaptable answer to the problems that will arise in the future when it comes to data processing requirements. The Hybrid system, which combines the greatest aspects of Spark and Hadoop, raises the bar for big data analytics and provides vital insights for businesses looking to realize every advantage of their data assets.