The data environments are gradually growing to become bigger and bigger. According to the recent surveys, the compound annual data growth through the year 2020 will tend to be near about 50 percent per year. With the increasing amount of data, the data sources are also raising. And along with that, the need for businesses to unlock those data and use them for taking critical decisions is even raising.
Businesses today need to understand complex data in-depth to unlock its potential and stand out. Complex data can be a curse for all organizations working in the Information Technology industry. It can lead to potential problems in managing data effectively and can also hinder system performance.
So what can be an effective solution to manage complex data? Well, these data complexities can be reduced to a larger extent by adopting Big Data Testing. Big Data consists of a huge amount of both structured as well as unstructured data collected from some sources including mobile devices and social media.
What Is Big Data Testing? Why Is It Needed?
In layman terms, Big Data indicates huge data quantities. The data comes from different directions, with the velocity and volume being monstrous. The data is replaced at high speeds, and so the requirement of processing also tends to increase, especially if in case it comes from social media. However, data can come from some sources and in many different formats.
In a data repository, one can find images, text files, presentations, audio files, emails, databases, video files, and spreadsheets. The structure and format of the data can vary depending upon the necessities. The data that come from digital repositories, social media, and mobile devices are both structured and unstructured.
The structured data are easier to analyze, while the unstructured data such as emails, video, documents, and voice are hard to analyze. Moreover, the unstructured data consume more resources. Relational Database Management System (RDBMS) can be an effective solution for managing data. It can define and manage data using structured query language (SQL).
However, RDMS fails to handle things, when data volume is tremendous. And if at all an organization would try to manage such huge data volume using RDMS, the process involved in doing so, will indeed be very costly. Hence, RDMS is not much efficient in handling complex data, and new technologies such as big data testing are needed to serve the purpose.
Advantages of Big Data Testing
Big data testing helps a lot in handling and managing complex data. Some of the advantages of Big Data testing are enlisted below:
- It helps in making sure that the huge sets of data across various sources are accurately integrated for providing real-time information.
- It facilitates quality validation of the data deployments for preventing wrong decisions and corresponding actions.
- It aids in data alignment concerning the changing dynamics for taking predictive actions.
- It helps to leverage the correct insight from even the smallest data sources.
- It ensures data processing and data scalability across the various touch-points and layers.
Major Areas of Big Data Testing
There are three major characteristics of Big Data testing. These are often referred to as the 3V’s.
Volume is one of the most critical characteristics of Big Data testing. Here, the testers deal with the incomprehensive data options. An example, in this case, can be Facebook, which has a huge capacity for storing photos. Facebook is believed to be storing around 250 billion pictures, and this number will keep on growing in the days to come as people on this social media platform upload around 900 million pictures a day.
Velocity is nothing but the measurement of the incoming data. Let us take the example of Facebook here once again. Think about the huge quantity of pictures it requires processing, storing, and retrieving.
There are different types of data including unstructured, semi-structured, and structured. You can have encrypted packages, images, sensor data, tweets, and many more. When the data is structured things are easy, but when the data is overflowing and unstructured, things need a lot more attention.
Big Data testing validates all the areas mentioned above and makes sure that data has been processed and is free of errors. It helps the testers to be confident about the health of the data. Big Data testing has indeed proved to be a boon to the businesses who earlier suffered from major data challenges, where the traditional data management systems never held good. Big Data testing has eliminated all these data challenges and made it easier for businesses to work with overflowing data. This new and advanced technology is evolving rapidly and will dominate the industry soon.