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Challenges and Mitigations in Big Data Testing

3 Mins read


Data has become the new gold, as is often bandied about in the media. The remark is not off the mark as data has seen a phenomenal expansion with the level of IT tools being leveraged by businesses worldwide. It has certainly become the new frontier to be conquered or dealt with by companies that look to gain insights into their business processes and workflows. The humongous quantum of data floating all around and generated from various sources is expected to reach 79 zettabytes in 2021 (Source: Statista). If that seems mindboggling to even fathom, then brace for the fact that it is going to double by 2025. However, one thing that needs to be taken with a pinch of salt is that 90 percent of the data swirling around is replicated, with only 10 percent considered to be unique.

So, when so much structured, semi-structured, and unstructured data is available from a variety of sources (social media being one), enterprises are struggling to make sense of it. Further, they not only need to sort out useful data from irrelevant or junk data but also need to analyze it. This is where big data testing comes to the fore as a challenge for enterprises that are looking to leverage these to become more competitive, productive, and innovative. In fact, enterprises need to understand how big data matters in their larger scheme of things and how to implement the insights derived from such data. Let us discuss the big data challenges that enterprises grapple with and the ways to mitigate them.

What are the challenges and suggested mitigations in big data testing?

As most data in the digital ecosystem is unstructured and heterogeneous, there are plenty of challenges in testing big data applications. Some of these are listed below.

High volume and heterogeneous data: With more business enterprises going digital, the quantum of data generated and received from various sources are increasing at an unprecedented rate. For instance, an eCommerce store may generate various types of data related to customer behavior, product sales, and others from sources such as the portal, social media, and even offline. And since the data received are not uniform and structured, processing, analyzing, and testing them demand time, effort, and resources. Also, the data extracted by businesses across sources are in the range of petabyte or exabyte, which can be a nightmare for testers to analyze and gain insights from the business perspective. Testing the entire volume of data, which is not consistent and structured, is certainly a challenge for the testers to grapple with.

The solution to such a problem is to simplify the large elements or data by breaking them down into smaller fragments and placing them in a manageable big data architecture.. This enables efficient use of resources, besides reducing testing time. Also, the test schemes should be normalized at the design level to obtain a set of normalized data for big data automation testing.

Test data management: As the test environment is often unpredictable, testers may face a host of challenges pertaining to the same. And it is only by leveraging complex tools, processes, and strategies that the 4Vs of big data can be addressed: volume, variety, velocity, and veracity. The recommendations for mitigations involve the normalization of tests and designs and dividing the data warehouse into small units to achieve higher test coverage and optimization.

Generating business insights: It has been observed that enterprises often focus on technologies to use rather than on outcomes. They are less preoccupied with what to do with the data. However, one of the most important reasons for analyzing and processing data is to derive meaningful business insights. So, for big data case study, the testers ought to consider scenarios such as identifying useful predictions, creating KPI-based reports, or making different kinds of recommendations. Also, testers preparing for data migration testing need to know the business rules and how different subsets of data are correlated.

Keeping costs under control: Getting access to cloud-based big data platforms and the augmented capability to deal with more granular data can drive up costs. Also, access to rich data sets can increase the demand for computing resources, which can be brought under control by opting for fixed resource pricing or implementing controls over queries.


Big data testing has gained prominence, and by leveraging the right strategies and best practices, any issues or bugs can be identified and fixed in the early stages. Also, typical objectives such as increasing the speed of testing, reducing the cost of testing, and generating meaningful business insights can be achieved.