The actual data is never stored on a namenode. Browse from thousands of Data questions and answers (Q&A). Apache Hadoop is a framework for distributed computation and storage of very large data sets on computer clusters. It is a component of Hadoop architecture which is responsible for storage of data.The storage system for Hadoop spread out over multiple machines as a means to reduce cost and increase reliability. Total nodes. The number of alive data … ( D) a) HDFS. (D) a) It’s a tool for Big Data analysis. 2.MapReduce Map Reduce is the processing layer of Hadoop. B. The namenode daemon is a master daemon and is responsible for storing all the location information of the files present in HDFS. It provides scalable, fault-tolerant, rack-aware data storage designed to be deployed on commodity hardware. Hadoop is an open source framework. The namenode maintains the entire metadata in RAM, which helps clients receive quick responses to read requests. Hadoop stores a massive amount of data in a distributed manner in HDFS. Which one of the following is not true regarding to Hadoop? Data is stored in distributed manner i.e. Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. . Each datanode sends a heartbeat message to notify that it is alive. 2) provide availability for jobs to be placed on the same node where a block of data resides. Technical strengths include Hadoop, YARN, Mapreduce, Hive, Sqoop, Flume, Pig, HBase, Phoenix, Oozie, Falcon, Kafka, Storm, Spark, MySQL and Java. ... the Name Node considers that particular Data Node as dead and starts the process of Block replication on some other Data Node.. 5. Apache Hadoop is a collection of open-source software utilities that allows the distribution of larges amounts of data sets across clusters of computers using simple programing models. Hadoop allows us to process the data which is distributed across the cluster in a parallel fashion. Before Hadoop 2 , the name node was single point of failure in HDFS Cluster. The data node is then responsible for copying the block to a second datanode (preferably on another rack), where finally the second will copy to the third (on the same rack as the third). 1. In the node section, each of the nodes has its node managers. c) HBase. Once we have data loaded and modeled in Hadoop, we’ll of course want to access and work with that data. Which of the following are NOT true for Hadoop? The Hadoop Distributed File System (HDFS) is the underlying file system of a Hadoop cluster. brief overview of Big Data, Hadoop MapReduce and Hadoop ... HDFS uses replication of data stored on Data Node to provide ... Data Nodes are responsible for storing the blocks of file It is a distributed framework. Hadoop: Any kind of data can be stored into Hadoop i.e. The Hadoop Distributed File System holds huge amounts of data and provides very prompt access to it. And each of the machines are connected to each other so that they can share data. In other words, it holds the metadata of the files in HDFS. A. It works on Master/Slave Architecture and stores the data using replication. Which technology is used to import and export data in Hadoop? Hadoop distributed file system also stores the data in terms of blocks. This is why the VerifyReplication MR job was created, it has to be run on the master cluster and needs to be provided with a peer id (the one provided when establishing a replication stream) and a table name. The Hadoop Distributed File System (HDFS) was developed following the distributed file system design principles. HDFS stands for Hadoop Distributed File System. 2. Hadoop began as a project to implement Google’s MapReduce programming model, and has become synonymous with a rich ecosystem of related technologies, not limited to: Apache Pig, Apache Hive, Apache Spark, Apache HBase, and others. However, the replication is quite expensive. b) It supports structured and unstructured data analysis. d) Both (a) and (c) HADOOP MCQs. If, however, the replication factor was higher, then the subsequent replicas would be stored on random Data Nodes in the cluster. It is done this way, so if a commodity machine fails, ... (Hadoop Yarn), which is responsible for resource allocation and management. Hadoop Interview questions has been contributed by Charanya Durairajan, She attended interview in Wipro, Zensar and TCS for Big Data Hadoop.The questions mentions below are very important for hadoop interviews. Endnotes I hope by now you have got a solid understanding of what Hadoop Distributed File System(HDFS) is, what are its important components, and how it stores the data. As the name suggests it is a file system of Hadoop where the data is distributed across various machines. Experimental results show the runtime performance can be improved by more than 30% in Hadoop; thus our mechanism is suitable for multiple types of MapReduce job and can greatly reduce the overall completion time under the condition of task and node failures. Data can be referred to as a collection of useful information in a meaningful manner which can be used for various purposes. b) Map Reduce. Apache Hadoop, a tool for analyzing and working with data. Be it structured, unstructured or semi-structured. Hadoop Base/Common: Hadoop common will provide you one platform to install all its components. The default size of HDFS block is 64MB. 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