‘Big-data’ is similar to ‘Small-data’, but bigger. It has bigger data that consequently requires different approaches, techniques, tools & architectures to solve new and old problems in a better way. Testing of such datasets is Big Data Testing.
Several companies do the depth analysis for their big data. It is seen that there are times when they fail to achieve the desired goal due to faults in data structure or due to the complex algorithms. Therefore it becomes important to do Big Data Testing.
Deployment of Big Data applications revolve around predictive analytics, organizations might face throng. Overall downtime is reduced by testing big data apps, as it improves the data quality and related processes of the application.
In the case of a large amount of data, chances of failure become high. Thus to avoid the failure, testing is considered as an integral part of application lifecycle to assure that the performance of the application is not affected by a small or big change in data sets.
Big data applications use the live data, there is a need for some filtering, sorting and analysis to ensure that the captured data is valid and useful. For these scenarios performance testing of the data ensure that the application processes accurate data in real-time.
Security and authenticity is the extreme importance for the enterprises that deal with the client application and host their data on their server. To maintain security and confidentiality, they have to perform big data testing at different levels to avoid any sort of security breach.