Why Big Data and Hadoop are recommended for Software Testers?

Testing processes have grown to become immensely important in every software domain that exists in the current market. Testing Engineer roles have begun to extend to innumerable domains when the enterprise decides to adapt itself to an enhanced technology. There are a wide array of reasons why Software Testing Engineers will find learning Big Data and Hadoop will benefit them greatly in the long run in their existing career, and also if they plan to switch to other adjacent domains.

Listed below are a few reasons why it is important for Big Data and Hadoop to be a part of the skillset held by Software Testers:

Career Growth

This may seem obvious considering the prevalence of Hadoop today, but there is a lot more to understand about how Hadoop and Big Data contribute to career growth of a Software Tester.  Hadoop jobs show exponentially higher growth rates when compared with other software testing jobs. Statistics from a recent study that more than 80% of individuals who take up Hadoop are known to be from a non-development background.

It is worthwhile looking at the limitations of current testing practices while testing applications needed to resolve Big Data issues. One such limitation is software testing approaches driven by data such as mismatch in size of data sets, and skewness in data. This should not be the case since the approach should be based on testing scenarios more than anything else. Another major limitation is that standard data matching tools such as win diff and more don’t tend to work with massive data volumes. This transforms into a huge limitation to the skillsets of software testing professionals.

Learn Quality Assurance from Industry Experts

Mid-sized data can be exposed as HBase tables and even verified from input data set by application of business logic on small sets of input.

Large-scale data can have Big Data techniques offer engineers with distinct skillsets which are utilized for the testing of complex and massive data sets along with finding various opportunities in fields of genomics, meteorology and even biological and environmental research.

Testing field Status Quo–Expert Speak

Experts in the field of testing have stated that organizations in today’s volatile market scenario sometimes view parts of their existing testing workforce as being expendable. This means that existing testers will have to step up their game in terms of highly applicable, niche and even transferable skillsets. This can be done by taking on Hadoop certifications which will set apart professionals from the herd of easily employable testing professionals.

Social media circuits have caught on to the fact that conventional testing is a breed that is fast fading with it being progressively replaced by highly integrated forms of testing. Such a dramatic transformation is bound to impact all professionals in the IT as well as IT-adjacent domains.

It is essential to analyse the job profile requirements to realize the complete significance of the wave of transformation that is sweeping over the testing profession currently in terms of skillset integration.

When looking at the above requirement, we can see that testing skills are largely needed and form the foundation of this job profile. Now, all that is needed of a Software testing engineer to transform into a Big Data or a Hadoop Tester is to update themselves with Big Data/Hadoop skills.

Hadoop Testers are usually responsible for most aspects of testing for systems driven by Cloudera Hadoop. Testers will also have to handle Day-to day System Builds, applying testing patches on a weekly basis and executing build checkouts. They would also have to execute preparation of formal testing documents like test plans, test cases and test scenarios along with the test results reporting.

Hadoop testers will also have to evaluate test results to decide compliance with the test plan. They will also have to collaborate with development teams to resolve complex and drawn out testing issues.  Also included will be the tracking and reporting of progress, performance, the risks and further issues.

 Register for Quality Assurance Live Webinar

Ease of Shifting to Big Data and Hadoop

• Flexibility to choose

Individuals proficient in Java will find that the transition to Big Data and Hadoop is a simple process because it is nothing but open-source Java based programming framework. Java has been used to write MapReduce scripts here. And also it is easily apparent that working on Hadoop requires a good deal of knowledge in Java.

The advantage had by Java experts does not imply hardship for non-Java professionals in testing since the major benefit in Hadoop is the fact that it has a wide range of tools, ready to be used by non-Java testing professionals and experts. A case in point would be Hadoop tools such as Hive, Pig and Sqoop which does not need Java knowledge since they depend heavily on SQL.

• Shared Skills and Application Platforms between a Testing professional and Hadoop professional

The concept of moving from an established skillset in a particular domain to a domain such as Big Data or Hadoop may not necessarily imply being overwhelmed by the transition. But one should realize the fact that, currently, Testing and Hadoop are not to interpreted as being mutually exclusive. But Testing and Hadoop are interwoven these days to produce efficiencies in testing that were previously unheard of.

Conclusion

While it is undeniable that Testing is undergoing a large scale transformation, one can rest assured that this won’t be the end of it. Also any good Testing Engineer or professional need possess strong technical skills, sharp analytical skills, be detail oriented and also have a great attitude that is geared towards continuous and progressive learning. All being said and done, Testing professionals have to evolve with the changing IT market and take up training and master their skills in Hadoop considering its overall set of features and flexibility.

Check out this insightful video on Introduction to Big Data Hadoop | Hadoop Tutorial for Beginners: