Data has become an essential part of every industry, organization and individual. With the advent of social media, the data is growing exponentially and needs to be managed so that it can be stored and retrieved quickly for faster processing. Here, 'Big Data' is a term used to identify the datasets that cannot be processed using traditional computing techniques. New technologies are required to store unstructured large datasets and processing methods. In this newsletter, we bring you the concepts that help us to understand the tools/techniques used to manage Big Data so that you can employ the right kind of tool as per your testing requirements.
Big Data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Simply, 'The Data beyond the storage capacity and beyond the processing limits' is Big Data.
Big Data is a problem statement that can be described in the image below:
Big Data Testing Challenges & Their Solutions
- Big Data testing is very different from traditional data testing in terms of Data, Infrastructure & Validation Tools
- Main stages of testing for Big Data applications are Data staging validation, MapReduce validation and Output validation phase
- Widely used testing tools for Big Data testing are: TestingWhiz, QuerySurge and Tricentis
- Important phase of Big Data testing is Architecture, as poorly designed system may lead to unprecedented errors and degradation of performance
- Performance testing for Big Data includes Data throughput, Data processing, Sub-component performance
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