Genius Ways of Big Data Testing | Calidad Infotech

2. Our Internet is an encapsulation of a lot of information. The internet stands firm over the information we input which is also data to get more information about something that is also data. Therefore, this advanced technology of the internet is data about data. Now, the presence of enormous data also requires churning and managing the same. In earlier days, the data files were small and concise. Hence, saving them for some time was not difficult. In our advanced world today, data management systems have come into force to swiftly handle big data. These big data applications are compatible with both structured as well as unstructured data. Now to ensure the quality of these giant data applications, strategic big data software testing is incorporated to know if they are up to the mark. Contact Us

3. What happens in Big Data Software Testing? Big data is a collection of numerous and enormous datasets. This giant data cannot be churned through age-old computer techniques or even outdated data testing methods. It is essential to have big automation tools as well as techniques that are compatible with the same to carry out data processing. At Calidad Infotech, we use renowned tools like Selenium, Cucumber, etc to carry out Big Data Software Testing Processes. What happens in Big Data Software Testing? The tools don’t work on their own to perform strategic big data testing unless we do. Thus, some of the big data testing methods are given below:-

4. 1. Functional Testing for QA of Data: In this type of testing, the front end of the application is thoroughly evaluated. The results generated through functional testing are put side by side with the expected results. This will help in identifying the gaps as well as deliver information about the application framework and its various components. 2. Performance Testing for QA of Data: When the injected data is put under automation, it allows us to check its functioning in various situations. Now, the performance of varieties of data as well different sizes of data in the application is accurately analyzed. Based on the performance, we can predict whether the components of the big data applications will be efficiently able to provide sufficient storage capacity, smoother processing as well as retrieval capabilities, especially for enormous data combinations. 3. Data Ingestion Testing: This testing will ensure whether the injected data is retrieved and loaded correctly, especially within big data applications. Once the ingested data is ensured of being error-free, it is can be loaded further into a big data framework.

5. 4. Data Processing Testing: In this testing, the automation tools will play a key role as well its performance over the ingested data can be analyzed. If the output files are in alignment with the business logic as well as the input files then it can be said the big data processing step was a success. 5. Data Storage Testing: When the data storage is tested, QA testers with the help of automation tools will analyze and validate if the output data is in line with the warehouse data. If the output data is correctly loaded as per the warehouse data then this storage testing is said to be a success. 6. Data Migration Testing: Data migration testing comes into play when the application jumps from server to server or undergoes any technological upgradation. In this type of situation when there is the transfer of data from an old to the new system, it is important to ensure that it takes place quicker but with no data loss.

6. What are the Difficulties and Solutions to Big Data Testing? When the unstructured data undergo testing, it faces various challenges as listed down below:- 1. Deficit and Diverse Data: Companies have storage of giant data to conduct their daily business. However, when they provide this data to QA testers, they cannot evaluate its accuracy through manual testing. In this situation, a strategy is devised wherein big data testing happens through automation. Moreover, specially designed automation tools also come in handy during this situation to evaluate voluminous data. 2. Challenges in Handling Voluminous Data: The key specialty of big data applications is to skillfully handle giant data. However, time and again, the application goes down and faces trouble in terms of accessibility, processing, or networking. In this situation, the segregation of data turns out to be advantageous. Bigger chunks of data can be divided into various nodes. Now moving forth, replicated files can be stored and classified under different types of nodes so that machine dependency on a single big chunk of data is reduced.

7. 3. Management of Testing Data: When QA testers carry out data testing, sometimes, it is complex for them to understand the tested data. As a result, it creates further problems in terms of storing big data within the data system. However, when QA testers work in a team along with marketing and development departments, it can help them in brainstorming and come to faster conclusions. As a result, it becomes simpler in terms of data extraction thereby filtering giant data. Calidad Infotech specializes in Big Data Testing to help you skyrocket your business. Our testers help businesses yield deeper insight into customers’ data so that your company can manage the data and yield a better ROI. Our proficient team of staff customizes applications for your business to effectively manage the workload. Contact us now to know more about the services we provide.

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