{"id":269,"date":"2021-07-12T17:16:29","date_gmt":"2021-07-12T17:16:29","guid":{"rendered":"https:\/\/www.wikitechy.com\/interview-questions\/?p=269"},"modified":"2021-09-14T09:47:12","modified_gmt":"2021-09-14T09:47:12","slug":"why-hadoop-used-for-big-data-analytics","status":"publish","type":"post","link":"https:\/\/www.wikitechy.com\/interview-questions\/big-data\/why-hadoop-used-for-big-data-analytics\/","title":{"rendered":"Why Hadoop used for Big Data Analytics ?"},"content":{"rendered":"<div class=\"TextHeading\">\n<div class=\"hddn\">\n<h2 id=\"why-hadoop-used-for-big-data-analytics\" class=\"color-pink\" style=\"text-align: justify;\">Why Hadoop used for Big Data Analytics ?<\/h2>\n<\/div>\n<\/div>\n<div class=\"Content\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<ul>\n<li><a href=\"https:\/\/www.wikitechy.com\/interview-questions\/hadoop\/what-is-big-data\/\" target=\"_blank\" rel=\"noopener\">Big data<\/a>\u00a0analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.<\/li>\n<li>Hadoop\u00a0is a framework to store and process big data. Hadoop specifically designed to provide distributed storage and parallel data processing that big data requires.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"TextHeading\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<h2 id=\"hadoop-is-the-best-solution-for-storing-and-processing-big-data-because\" class=\"color-blue\">Hadoop is the best solution for storing and processing big data because:<\/h2>\n<\/div>\n<\/div>\n<p style=\"text-align: justify;\">Hadoop stores huge files as they are (raw) without specifying any schema.<\/p>\n<div class=\"Content\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<ul>\n<li><b>High scalability<\/b>\u00a0\u2013 We can add any number of nodes, hence enhancing performance dramatically.<\/li>\n<li><b>High availability<\/b>\u00a0\u2013 In\u00a0<a href=\"https:\/\/www.wikitechy.com\/interview-questions\/apache-pig\/what-is-the-advantages-of-pig-in-hadoop\/\" target=\"_blank\" rel=\"noopener\">hadoop<\/a>\u00a0data is highly available despite hardware failure. If a machine or few hardware crashes, then we can access data from another path.<\/li>\n<li><b>Reliable<\/b>\u00a0\u2013 Data is reliably stored on the cluster despite of machine failure.<\/li>\n<li><b>Economic<\/b>\u00a0\u2013 Hadoop runs on a cluster of commodity hardware which is not very expensive.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"text-center row\" style=\"text-align: justify;\"><\/div>\n<div class=\"TextHeading\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<h2 id=\"what-is-hadoop\" class=\"color-purple\">What is Hadoop ?<\/h2>\n<\/div>\n<\/div>\n<div class=\"Content\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<ul>\n<li><a href=\"https:\/\/www.wikitechy.com\/interview-questions\/apache-pig\/what-is-the-difference-between-pig-hive-and-mapreduce\" target=\"_blank\" rel=\"noopener\">Hadoop<\/a>\u00a0is an open source project from Apache Software Foundation.<\/li>\n<li>It provides a software framework for distributing and running applications on clusters of servers that is inspired by Google\u2019s Map-Reduce programming model as well as its file system(GFS).<\/li>\n<li>Hadoop was originally written for the nutch search engine project.<\/li>\n<li>Hadoop is open source framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware.<\/li>\n<li>Hadoop can be setup on single machine , but the real power of Hadoop comes with a cluster of machines , it can be scaled from a single machine to thousands of nodes. Hadoop consists of two key parts,\n<ul>\n<li>Hadoop Distributes File System(HDFS)<\/li>\n<li>Map-Reduce.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"ImageContent\" style=\"text-align: justify;\">\n<div class=\"hddn\"><img decoding=\"async\" class=\"img-responsive center-block aligncenter\" src=\"https:\/\/cdn.wikitechy.com\/interview-questions\/hadoop\/hadoop-overview.png\" alt=\"Hadoop Overview\" \/><\/div>\n<\/div>\n<div class=\"TextHeading\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<h2 id=\"hadoop-distributed-file-systemhdfs\" class=\"color-blue\">Hadoop Distributed File System(HDFS)<\/h2>\n<\/div>\n<\/div>\n<div class=\"Content\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<ul>\n<li>HDFS is a highly fault tolerant, distributed, reliable, scalable file system for data storage.<\/li>\n<li>HDFS stores multiple copies of data on different nodes; a file is split up into blocks (Default 64 MB) and stored across multiple machines.<\/li>\n<li>Hadoop cluster typically has a single namenode and number of datanodes to form the HDFS cluster.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"TextHeading\" style=\"text-align: justify;\">\n<div class=\"hddn\">\n<h2 id=\"map-reduce\" class=\"color-blue\">Map-Reduce<\/h2>\n<\/div>\n<\/div>\n<div class=\"Content\">\n<div class=\"hddn\">\n<ul>\n<li style=\"text-align: justify;\">Map-Reduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks.<\/li>\n<li style=\"text-align: justify;\">It is also a paradigm for distributed processing of large data set over a cluster of nodes.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Answer : Big data analytics is the process of examining large data&#8230;<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1065],"tags":[195,1119,360,1041,1168,1170,1175,1179,1174,1034,203,199,214,1120,205,1173,948,947,485,222,484,1171,1172,1169,1176,1177,1178,196,212,286,970,366,288,367,206,975,200,974,197,280,364,1031,1167,968,216,285,1121],"class_list":["post-269","post","type-post","status-publish","format-standard","hentry","category-big-data","tag-accenture-interview-questions-and-answers","tag-att-interview-questions-and-answers","tag-atos-interview-questions-and-answers","tag-big-data-analytics","tag-big-data-hadoop","tag-big-data-hadoop-certification","tag-big-data-hadoop-tutorial","tag-big-data-notes","tag-big-data-toolshow-big-data-and-hadoop-are-linked","tag-big-data-tutorial","tag-capgemini-interview-questions-and-answers","tag-casting-networks-india-pvt-limited-interview-questions-and-answers","tag-cgi-group-inc-interview-questions-and-answers","tag-collabera-technologiesinterview-questions-and-answers","tag-dell-international-services-india-pvt-ltd-interview-questions-and-answers","tag-difference-between-big-data-and-data-science","tag-difference-between-big-data-and-hadoop","tag-difference-between-hadoop-and-spark","tag-ernst-young-interview-questions-and-answers","tag-flipkart-interview-questions-and-answers","tag-genpact-interview-questions-and-answers","tag-hadoop-architecture","tag-hadoop-as-big-data-solution","tag-hadoop-database","tag-hadoop-example","tag-hadoop-modules","tag-hadoop-storage","tag-ibm-interview-questions-and-answers","tag-indecomm-global-services-interview-questions-and-answers","tag-lt-infotech-interview-questions-and-answers","tag-mindtree-interview-questions-and-answers","tag-netapp-interview-questions-and-answers","tag-r-systems-interview-questions-and-answers","tag-rbs-india-development-centre-pvt-ltd-interview-questions-and-answers","tag-sap-labs-india-pvt-ltd-interview-questions-and-answers","tag-tata-consultancy-service-interview-questions-and-answers","tag-tech-mahindra-interview-questions-and-answers","tag-trigent-software-interview-questions-and-answers","tag-unitedhealth-group-interview-questions-and-answers","tag-virtusa-consulting-services-pvt-ltd-interview-questions-and-answers","tag-wells-fargo-interview-questions-and-answers","tag-what-is-big-data","tag-what-is-hadoop-used-for","tag-wipro-infotech-interview-questions-and-answers","tag-wipro-interview-questions-and-answers","tag-xoriant-solutions-pvt-ltd-interview-questions-and-answers","tag-zs-associates-interview-questions-and-answers"],"yoast_head":"<!-- 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