Big data analysis is the skills and the ability needed to manage and study large amounts of data in order to determine patterns, and trends, secure competitive advantages, and improve customer satisfaction.
By large amounts of data, we refer to volume, velocity, and variety, which are the cornerstones of Big Data.
Volume represents large amounts of data, for example, a survey; Velocity means how fast the data is created, for example, Twitter messages or Facebook comments; and Variety means the complexity of the data, for example, audio, video, and text messages in Whatsapp.
In addition, we use the term Veracity, which is related to the quality of the data being analyzed for example, in the medical field when large studies are done to obtain results on medicines.
How can a QA engineer assure and add value to the analysis of all this information?
Big data often finds important challenges that improve the risk of the outcome and the decisions that are taken. From the exponential growth of data to the security of the information, the spectrum is overwhelming and leads to not taking any chances and continuing to work as usual.
As QA engineers this challenge can be too much to handle, however by defining a proper strategy, the risks and stakes will be addressed properly and will ensure an exceptional outcome and deliver customer satisfaction.
On one hand, the most powerful tool in the hands of a QA Engineer to achieve Big Data’s goal is the use of metrics. Being able to determine a powerful metric, and all its components will improve business processes, detect any important issues on time, and most importantly guarantee customer satisfaction. After obtaining all the required information, the second most important skill needed from the QA is the ability to analyze and deliver the results obtained in a user-friendly manner, first by delivering what is expected for the customer and secondly by assuring high quality, reliability, and security of the information.
QA as known doesn’t apply testing cycles, nor creates bugs or defects, we are not expected to behave as usual, rather compile a lot of information, analyze it, and deliver results that have been filtered to mitigate the most important challenges that cause uncertainty to the customer.
Pairing the technical skills with the customer’s mindset will help the QA Engineer to define the metrics needed to obtain results that not only will create value, but will put a smile on the customer’s face and most importantly, put them ahead of the competition.
A good book praises itself, so this is the best chance for a QA Engineer to get their creativity out, understand what is needed, and deliver via metrics valuable information.
You’ve got your work cut out for you but tools will eventually come to help and automate the processes, leading to the optimization of processes and delivering better and faster results.
We should be on the lookout for these tools and start working with them.
This is just the tip of the iceberg but when it comes to tools available, PySpark seems to be one we should be looking at.
It’s often expected that a QA engineer should have the technical skills and mindsets of a developer, but with these new technologies, the expectations will turn into requirements, as the figure of automated processes and programming tools will lead and deliver the results faster than we could expect.
For Big Data, as of this moment, Python is the way to go, and preparation and skills will be needed to apply automated tools and deliver high-quality outcomes to assure customer satisfaction.
The best fuel we can use to explore these opportunities is our thirst for challenges and our call to assure quality on the highest level in everything that is done.
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