Testing is a challenging activity as it is. QA departments deal with lack of resources, tools and time on a daily basis. Surely, with tremendous popularity testing, as a truly necessary activity has gained, the community has found solutions to majority of issues that are to be faced. Experiments have led to astonishing results and some fine practices were developed to guide and assist both the testing and the business side. However there are still issues with no clear solutions in existence. They may be dealt with when they emerge, however one cannot prevent them from happening.
Big Data = Big Trouble
Big Data is vital to a business, however, considering all the value it presents testing gets even harder. Big Data is probably one of the hardest things any test engineer may work with. Why? It’s not really structured in a way normal data would be. It’s either semi-structured or does not have any particular structure at all. And all that chaos is being contained in rows or cells or anything else of that matter making things even worse. If you want the cherry on top of this cake - real-time testing is required by today’s industry and that is a lot of work considering Big Data operates with Terabytes of information!
How bad is the patient?
Fortunately we already have solutions to most issues and are currently working on improving them in the testing community. But, there are still 2 big guns you can’t live without causing severe damage. Why are they so potentially harmful? Because they are the things you can’t decently test Big Data without visualization and automation. Here is a little deeper view on the issue:
- Visualization: testing in virtual environments may be one of the greatest creations man has come up with. Making sure everything works before going live and in a simulation of a real-deal environment? What can be better? Well, that noted, here is what you will be facing before you gain ay results whatsoever:
- You may receive a lot of headache from virtual machine latency, especially at times real-time testing is required.
- Images are even harder! Imagine all those Terabytes of data, and if they are images no machine can truly understand (so far) simply imagine explaining all that data to a virtual machine, while performing input?
- Virtual machines are hard to manage as they are.
- Automation: and there we have it, our strongest ally is our greatest enemy at the same time. Surely no explanations of test automation value are required so we will simply skip to the challenging parts:
- Tools designed for automation are still machines and are tailored to look out for some commonly expected problems. The structure Big Data has makes this quite more difficult. Tools will not handle some issues Big Data might have and that is a serious issue itself.
- You will require really skilled testers to pull everything off, with deep coding and scripting skills and vast experience in both Big Data and Test Automation. Such people are really hard to find.
- And, surely, there is more complex software that requires appropriate management.
However you are now prepared, meaning armed to face all potential issues, or, you can simply outsource the kinds of projects; after all, there are many companies with appropriate tools and expertise available. We are a great example of such a company, by the way. Good luck with your Big Data!
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