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Mastering the maze with Venkateshwarlu Rupani: performance testing in big data analytics

Data continues to increase exponentially, and organizations are vying to take advantage of the best big data analytic systems on the market to gain the competitive edge they need to thrive in cut-throat industries.

Photo courtesy Venkateshwarlu Rupani
Photo courtesy Venkateshwarlu Rupani

Opinions expressed by Digital Journal contributors are their own.

The need for big data testing in analytics is growing. Data continues to increase exponentially, and organizations are vying to take advantage of the best big data analytic systems on the market to gain the competitive edge they need to thrive in cut-throat industries. 

Unfortunately, the performance testing of their vast data systems is currently lackluster at best. Complex challenges are no match for traditional testing strategies. The unique intricacies of big data leave testing methods in their wake and organizations with a lack of understanding and information that can be crucial to their success. 

Performance testing is essential for reliable big data analytics. Diverse data types, varied technological components, and rapid data throughput all require advanced and sophisticated analytics that have been conceptualized but have yet to be created and implemented. This leaves organizations looking at a barrage of data that analytical systems simply can’t keep up with. As the digital realm continues to expand, having a robust testing strategy is indispensable for harnessing the full power of big data. 

Big data relates to data creation, storage, retrieval, and analysis—and performance testing is vital to success. If part of the system isn’t operating properly and isn’t caught by an adequate testing system, countless transactions can be conducted problematically. Before a business owner can catch it, there could be an absolute wealth of mistakes carried out, costing their company copious amounts of resources and negatively impacting their reputation. Unfortunately, some companies can’t revive their business after a hit from an underperforming system. 

The need continues to grow. Data systems are becoming increasingly complex, handling more data every day. With performance testing already lagging behind the current demand, it’s impossible to know if it can gain enough traction to even things out. We need to see impressive leaps and bounds in the innovation of performance testing to accommodate the current and increasingly elevating needs of companies that count on data systems to keep them afloat.

The best way to start to resolve the gap in efficacy is to stick to a solid strategy, utilizing testing approaches that are most compatible with the system in question. We can get the best analysis by tailoring the approach to specific types of data and operations at hand. Moving away from traditional methods and following the current best practices is the best move we can make. As developments continue to be made in this sector, imperative improvements will be implemented one by one. Before we know it, the vast valley of incompetence will be closed. Then, performance testing will be able to accomplish what it was intended to do, operating on a design that allows it to fulfill its function. 

We’re keeping a keen eye on each promising development in the industry and carefully curating the next best approach to help companies stay on top of significant amounts of data. It’s important to remember that the best practices in the industry are always evolving, so watching for the latest innovations is essential to keep that competitive edge you’re looking for.

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Jon Stojan is a professional writer based in Wisconsin. He guides editorial teams consisting of writers across the US to help them become more skilled and diverse writers. In his free time he enjoys spending time with his wife and children.

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