As data information is fetched from various sources, it needs live integration, for it to be useful. This can be achieved by extensive testing of the data sources to make sure that the application does not have a scalability issue. Together with this, the application has to be tested thoroughly to enable live deployment.
The most imperative element for a tester, testing a big data application is the data itself. While testing Big Data applications, the tester wants to dig into semi-structured or unstructured data with changing schema. All these applications cannot be tested through ‘Sampling’ the same way in data warehouse applications. As Big Data applications comprise of very large data sets, testing has to be carried out with the help of appropriate research and development. So how should a tester go about testing the Big Data applications?
Real-time, interactive and batch; though they all comprise of movement of data. Thus, all big data testing strategies are based on the transform, extract, and load (ETL) process. It originates by validating the data quality impending from the source databases, validating the transformation or process by which the data is structured.