How Does Big Data Testing Impact an Organization?

Through this article, we’ll gain complete knowledge about the importance of big data testing for business organizations. In order to gain edge over their competitors and make wiser decisions, business enterprises are continuously looking for new methods to harness the power of big data as the number of big data applications worldwide is increasing substantially. Therefore, it is essential that these apps must be tested quite regularly. Hence, the use of big data testing approach have become important and crucial in testing big data applications.

Now before proceeding into the further discussion to know about the significance of big data testing for business enterprises; let’s know the concept of big data testing briefly which is essential in order to test big data software applications for business enterprises.

Table of Contents

The Concept of Big Data Testing

The process of examining and validating the functionality of a big data application is known as big data testing. We are all aware that big data is a collection of data that is so large in terms of volume, variety, and speed that it cannot be processed by itself using conventional system testing methods.

Additionally, testing of the datasets would require unique testing methods, exceptional platforms, brilliant strategies, and a variety of tools. This kind of testing aims to guarantee that the system functions efficiently, effectively, and securely while being error-free.

The Significance of Big Data Testing

Big Data is a phenomenon that is expanding dramatically over time and contains unprocessed but extremely valuable information that can alter the course of any business organization. Basically, it is a collection that reflects a sizeable dataset and can be stored in a business database or gathered from other sources. Let’s look at a real-world example where major corporations like Amazon & Ikea are using big data to their advantage by gathering information about customer purchasing habits at their stores, internal stock levels, and inventory demand-supply relationships.

Hence, with the help of big data testing, the businesses can evaluate this data quickly, sometimes even in real-time to improve their client’s experience. Therefore, in order to properly test big data applications, big data is essential. The purpose of performing big data testing on a software is to process data and deliver a desired result based on applied logic. Since the logic’s implementation is entirely reliant on business requirements and data, it must be tested before going into production.

The Process of Big Data Testing

The complete process of big data testing can be divided into the following three stages: –

1) Pre-Hadoop Stage – Process validation makes up the Pre-Hadoop stage of big data testing, which is also known as the initial stage. Here, data validation is important and necessary to ensure that the information gathered from multiple sources, such RDBMS and weblogs, is accurate before it is incorporated to the system.

2) Map-Reduce Stage – The second stage is Map Reduce validation. Here the tester validates the business logic on every node to ensure that the Map Reduce process is flawless.

3) Output Validation Stage – The third and last stage of big data testing is the data output validation process. In this process, output data files have been produced and are prepared for transfer to an Enterprise Data Warehouse or other suitable system, depending on business requirements.

The Challenge of Big Data Testing

The large data hubs or data marts hold enormous amounts of data that are generated everyday. Therefore, there is a need for effective storage and a method of processing data in an efficient manner. If we think about the telecom sector, it produces a lot of call logs everyday and needs to process them to improve customer service and compete in the market. The same applies to the validity of test data; it must be comparable to production data and must include all logically valid fields.

Making test data that is comparable to production data becomes difficult when testing big data applications. Any big data application’s primary demand is performance, which is naturally why businesses are turning to No SQL technologies, which can process their massive data in the shortest amount of time. A large dataset should be processed in at least a reasonable amount of time.

Performance testing is a difficult process in large data testing since it necessitates monitoring of cluster nodes while being executed and demands time for each execution iteration. Hence, big data testing is necessary in order to make sure that the big data application is functioning properly.

The Conclusion

Hence from the above discussion, we can conclude this article by saying that big data is the trend that is reshaping society and its institutions because of the opportunities it offers to utilize a broad range of data in significant quantities and quickly.

The method of big data testing will make it simple for a test engineer to confirm and certify the implementations of business requirements, and for stack holders, it saves a tremendous amount of money that must be invested to attain the anticipated business returns.

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