Why should QA start learning bigdata?

Quality assurance engineering is testing the code identifying the bugs, testing the fixed code, making sure the product meets the quality requirements before being implemented in production or in a customer site. With changing trends quality assurance has evolved from manual testing to automated testing using selenium test suite. Current requirement demands software engineer in test as opposed to quality assurance engineer. With changing trends and growing technology quality assurance engineer is expected to possess knowledge of operating system stack, databases, network as opposed to simple testing of applications. Bigdata is the talk of the town and having a good grep on big data technology stack will help the quality assurance engineers find a role as big data QA. What are the components that make up big data? The popular framework that supports bigdata is hadoop. Hadoop the apache open-source project has lots of components. Each and everyone is a different project - HDFS, YARN, Mapreduce, Hive, HBase, OOzie,workflow etc. Each and every big data component has a specified purpose As a QA engineer in big data environment the typical expectation would be 1) Have thorough knowledge on all the big data components 2) Know how to access Amabri or HUE aka hadoop user experience to manage all the hadoop components 3) Access the data feeds, make sure the distributed processed output is as expected 4) Good knowledge of hadoop fs commands to manipulate files, folders in HDFS