Big Data, Bigger Challenges: A Comparative Study of Performance Testing

To make sure big data systems are reliable and effective, performance testing is essential. In light of big data applications, this study offers a comparative review of important performance testing methodologies. The methods discussed include capacity testing, compatibility testing, load testing, stress testing, scalability testing, performance profiling, benchmark testing, real-time testing, endurance testing, and concurrency testing. Every method is assessed according to its advantages and disadvantages, providing a comprehensive comprehension of how well-suited each is for certain situations. Scalability testing guarantees that systems can effectively handle increasing workloads, whereas load and stress testing evaluates a system’s resilience and responsiveness under various pressures. Enhancing system efficiency through performance profiling and real-time testing are essential for low-latency data processing. Capacity and compatibility testing guarantee smooth operation across various platforms, while benchmark and endurance testing offer benchmarks for performance comparisons and confirm system stability. Testing for concurrency evaluates the capacity for concurrent processing, maximising resource use. These techniques are essential for identifying system problems, improving performance, and providing insightful information, even if they are not quantified by conventional accuracy measurements. It is anticipated that future research in this area will address current issues such as practical testing scenarios, tool scalability, privacy, security, and machine learning integration for predictive testing.