When it comes to information, the current buzzword and analysis trend these days is definitely big data. The advent of the World Wide Web meant that we are constantly being bombarded with a lot of data, which is something that requires immediate processing in order to gain a better understanding of it. Enter big data, which is extremely helpful in handling huge amounts of data that need managing. More and more businesses these days are looking at big data as a way of streamlining the complexities that go with data processing, and have seen its great returns when it comes to being a tool for growth and expansion. Therein comes the importance of discussion about the details strategy for the big data performance testing.
These days, the opportunities and growth challenges that come with data process engineering are three-fold: aggregating the volume (upping the amount of data), increasing the speed of data that comes in and out (velocity), and amassing the variety or data types as well as sources. We can call this the 3V model for volume, velocity, and variety.
Big data performance testing touches on how well the system performs in order to churn out data that is useful to the business, and not just managing the integrity and complexities of data itself. Much of one’s investment should be applied on framework performance engineering, failover, and data rendition.
A word: It is important to conduct architectural testing before anything else because systems that are inadequate or poorly designed have a high probability of resulting in performance degradation. As such, these are the three strategies which are also the basic Big Data systems, that one must implement when it comes to performance testing of the big data systems.
Because the data is highly complex (dealing with large volumes of unstructured and structured data), one should be mindful of applying these things to testing:
Since the system is made up of multiple components, it is important to conduct testing in isolation and starting out at component levels prior to testing them all at the same time. Thus, performance testers must be well-versed when it comes to their knowledge of framework and technology in big data. This will also entail the use and application of market tools such as:
It may seem like big data performance testing is challenging, but having the right tools, strategies and skill sets will definitely allow you to manage everything smoothly!
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