Optimizing Database Management Systems for Big Data Applications: Techniques and Challenges

The extraordinary growth in data-driven critical changes how Database Management Systems (DBMS) need to function. Modern Database Management Systems have become inadequate because it has no longer meet the needs of big data requirements, which include massive scales and rapid data processing. The research analyzes the development of DBMS technologies designed for big data systems by examining three essential challenges, which include scalability needs as well as performance improvements, and handling different kinds of data. In order to increase system speed and response time, the study looks at optimizing system performance using query optimization, data compression, parallel processing, and vectorization techniques. The paper examines major data security matters and system timing issues, which both present significant performance challenges during increasing data growth. This paper evaluates upcoming distributed computing frameworks and machine learning strategies, and automated database tuning through AI, which can empower the next level of DBMS solutions. The research survey explores optimal techniques for DBMS systems and defines upcoming research routes that focus on developing big data DBMS performance.