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Are You Big Data Testing? 5 Tips to Remember

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Big data has made its triumphant entrance at pretty much every business, non-profit organization and startup in the world today. It is at the forefront of every industry, and if it isn’t embraced then you will likely get left behind and lose out on an incredible opportunity.

The idea of big data was pretty much at the back of everyone’s mind years ago. Fast forward to the present, and everyone is talking about big data without really knowing what it is exactly.

In addition to not fully comprehending what big data is all about, corporations are tackling the issue blindfolded. Whether it is managing big data or testing big data, private firms need to get a handle on this trend before it becomes too immense for your enterprise to handle or grasp.

Manpower, tools and resources are necessary in your firm’s plight to big data testing.

Here are five things you need to remember when you’re big data testing:

It’s Time to Adopt New Technologies

Big data is new and is a product of the 21st century. If your company is still employing Windows XP on a massive desktop with a huge monitor then you won’t be able to grasp and exploit big data. In other words, you must adopt new technologies as soon as possible in order to actually test big data and take advantage of the aspects you discover in the gigantic pile.

Do Your Testers Have Knowledge of Tools?

Once you have the tools in your workplace, you need to find out if your manual big data testers have a rudimentary understanding of the tools you have adopted. This is crucial because these will be tools that will be utilized to test the big data and extract all of the information pertinent to your overall organization.

Keep Your Big Data Intact in Your Environment

A common difficulty is ensuring your big data is intact in your testing environment.

Throughout the data testing phase, there will always be one tester who will be responsible for some of the builds. In other parts of the phase, there will be multiple testers accessing the same data and will perhaps customize the data. This makes it hard to keep the big data intact.

You have two options here that you must incorporate into the testing protocol:

  • Make copies of the big data and maintain personal copies of the same data.
  • Ensure that your big data is not corrupted and that the application can read the data source.

These two policies will make certain that your big data will stay together.

Security Testing for Big Data

Security testing for big data can be hard to conduct because the process consists of determining if the system can actually protect data from nefarious means. In order to make the process a bit easier, you need to cover these four primary topics:

  • Authentication
  • Authorization
  • Confidentiality
  • Integrity

These topics are integral to the security and integrity of big data.

Gradually Include Automation in Big Data Testing

As soon as you grasp the basics and establish a path to big data testing, you will inevitably come across the coquettish nature of test automation. Many elements of big data testing can oftentimes be tedious and mundane and requires neglect of other areas. Automation can circumvent this.

By utilizing the best tools around, you can automate UI testing, performance, manual units and accessibility. At the same time, if something goes wrong, you need to automate the notification of engineers and the process of tasking. This way, if a failure arises, it can be manually rectified.

Final Thoughts

Akin to cloud computing, the average person, or enterprise, would be unable to accurately describe big data. Perhaps not even Dilbert could give a satisfying response to the question: what is big data?

With that being said, ignorance is never an excuse, especially when your company’s sustainability and competitiveness is at stake.

Big data testing is here to stay, and you need to get all of your testers on board with the policies in place to streamline operations and processes. With the right tools, resources and know-how on hand, you can fully maximize the chaotic world of big data and its testing.