Your big data testing strategy should include big data testing techniques, big data testing methods, and big data automation tools.
Join the DZone community and get the full member experience. Consider the internet like Pandora’s Box. The internet has become so powerful because of data and data about data. For example, when you search the internet for something, you search for data about data. And a search proves to be useful only because someone preserved this information you were seeking somewhere over the internet. As technology advanced to support all this data, so too did the demand for big data applications. In earlier times, data was preserved by using simple data files. As the complexity of data grew, database management systems came into existence. Soon both structured and unstructured data were being generated at a giant level, gaining the title “BIG DATA.” This big data testing tutorial can guide you through the process of creating a big data testing strategy, discusses the best tools for big data testing, and answers your most pressing QA testing questions, including: Big data testing is the process of data QA testing big data applications. Since big data is a collection of large datasets that cannot be processed using traditional computing techniques, traditional data testing methods do not apply to big data. This means your big data testing strategy should include big data testing techniques, big data testing methods and big data automation tools, such as Apache’s Hadoop. Get the most out of this big data tools tutorial by first exploring how to QA data of gigantic size. After reviewing big data case study after case study, you can expect to find that successful teams include the same types of big data testing methods. Is your team ready to explore how to QA data? This big data tools tutorial advocates the inclusion of the following tests within your data QA strategy. Functional Testing: Front-end application testing provides advantages to data validation, such as being able to compare actual results produced by the front-end application against expected results as well as gaining insight into the application framework and its various components. Performance Testing: Automation in big data allows you to test performance under different conditions, such as testing the application with different varieties and volumes of data.
