Big Data Testing: Key Challenges and How to Deal With Them

To say that data is a foundational element of the modern world would be a gross understatement. Today, pretty much everything relies on data, especially new-age companies which have come to rely on big data testing in order to keep things moving. Big Data Testing is quite popular today because of its various properties like volume, velocity, variety, variability, value, complexity & performance that puts forth several challenges. And, with the click of a button, one can generate megabytes of data. Testing such large collections of various data types ranging from tables to texts and images is a challenge.

Unfortunately, big data testing tool is subject to some challenges. Here is an overview of these challenges and how to overcome them.
1. Volume: We know, we know — the entire point of big data is, well, dealing with an abundance of data given that modern businesses must contend with petabytes and exabytes of data to ensure continuity. Now, testers must not only test such humongous amounts of data but first must also verify the data is appropriate for being involved in driving decisions. As you can imagine dealing with improperly labeled data, then, is rendered a Herculean task.
2. Requisite expertise: It is no secret that technologies and the many, many tools they give us evolve at a mind-boggling pace. This holds in the context of big data testing as well, wherein testers must keep up with the new algorithms, parameters of different manners of testing, etc. All these factors can also impede the setting up of automated testing processes. Thankfully, the simplest fix to this particular challenge is coordination amongst the development team, marketing team, and testing team to ensure testers have a complete understanding and a bird’s eye view of the extraction, filtering, and processing of the data involved.
3. Sentiments: The main data feed for a big data testing solution is typically supplemented by data sources from social media platforms, text documents, etc. These sources provide what is generally referred to as unstructured data. Handling the emotions that are generally deeply integrated with such unstructured data is what proves to be a challenge when it comes to big data testing. Hence, to properly utilize said data, the testing team must appropriately encapsulate the emotions and transform them into relevant insights to help drive informed business decisions.
4. Technologies: More often than not, companies overlook the importance of verifying their technological capabilities to successfully deal with the task at hand. While tools and solutions are abundant on offer in the market, not every tool is well suited for every task or type of big data testing. Hence, it is important to first precisely understand your organization’s big data testing requirements and then take the time to carefully evaluate said requirements against the solutions available in the market.

There is certainly no denying that big data is, and will remain for the foreseeable future, a highly capable tool that drives wondrous results for companies of all scales and sizes. However, the ability of this tool to deliver the desired results depends a lot on the data and a variety of pertinent factors relevant to said data. The above discussion demonstrates that while big data testing is prone to some challenges, it can be easily addressed if teams make sure to practice some caution and integrate specific strategies relevant to the challenges they face. Nonetheless, if you want to ensure top-notch results for your big data software testing projects, we highly recommend engaging the services of an expert to help you successfully navigate the process.