Big Data Hadoop Interview Questions And Answers 2016

Big Data Hadoop is a continually changing field which requires people to quickly upgrade their skills, to fit the requirements for Hadoop related jobs. If you are applying for a job role, it is best to be prepared to respond any Hadoop interview question that may come your way. Big Data Certification Training is designed to prepare you for your next project in the world of Big Data. Hadoop is the market leader among Big Data Technologies and it is a chief skill for every professional in this field. Spark is also gaining significance with an emphasis on real-time processing. As a big data professional these are mandatory skills. To help you get on track, here are few of the sample Interview Questions asked during latest Hadoop job interviews 1. What are real-time industry applications of Hadoop? Hadoop, popularly known as Apache Hadoop, is an open-source software stage for scalable and distributed computing of large volumes of data. It offers rapid, high performance and lucrative analysis of structured and unstructured data generated on digital platforms and inside the enterprise. It is used in approximately every sections and sector today. 2. How is Hadoop dissimilar from other parallel computing systems? It is a distributed file system, which lets you save and handle the massive amount of data on a cloud of machines, managing data redundancy. The primary advantage is that since data is stored in several nodes, it is superior to process it in distributed manner. Each node can process the data stored on it instead of spending instance in moving it over the network. 3. What all modes Hadoop can be run in? It can run in three modes: Standalone Mode: Default mode of Hadoop, it utilizes local file system for input and output operations. This mode is mainly used for a debugging reason, and it does not hold the use of HDFS. Further, in this mode, there is no custom configuration necessary for mapred-site.xml, core-site.xml, hdfs-site.xml files. Pseudo-Distributed Mode (Single Node Cluster): In this case, you need configuration for all the three files mentioned above. In this case, all daemons are operating on one node and thus, both Master and Slave node are the same. Fully-Distributed Mode (Multiple Cluster Node): This is the production stage of Hadoop where data is used and distributed across several nodes on a Hadoop cluster. Separate nodes are allotted as Master and Slave. What is distributed cache and what are its benefits? Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files when needed. Learn more in this MapReduce Tutorial now. Once a file is cached for a specific task, Hadoop will make it available on each data node both in the system and in memory, where the map and reduce tasks are executing. In a while, you can easily access and interpret the cache file and fill any collection in your code.