Choosing the most suitable one is a\nchallenge when several big data frameworks are available in the market. The\ntraditional approach of comparing the strength and weaknesses of each platform\nis to be of less help, as businesses should consider each framework with their\nneeds in mind.<\/p>\n\n\n\n\n\n\n\n
Here we are attempting to answer a pressing\nissue: which to choose-Hadoop MapReduce or Spark.<\/p>\n\n\n\n
Hadoop and Spark are big wigs in big data analytics. Both are open source<\/strong><\/a> projects by Apache Software. Hadoop<\/strong><\/a> has been a market leader for the past five years. Based on recent market research, Hadoop\u2019s installed base includes more than fifty thousand, while Spark has ten thousand installations only.\u00a0<\/p>\n\n\n\n Nevertheless, Spark\u2019s reputation soared in\n2013 to beat Hadoop in only a year. To make the comparison equitable and fair,\nwe will compare Spark with Hadoop MapReduce, as both are responsible for data\nprocessing. <\/p>\n\n\n\n In fact, the major difference between Hadoop\nMapReduce and Spark is in the method of data processing: Spark does its\nprocessing in memory, while Hadoop MapReduce has to read from and write to a\ndisk. Hence, the speed of processing differs significantly- Spark maybe a\nhundred times faster.<\/p>\n\n\n\n However, the processed data volume also\ndiffers. Hadoop MapReduce can work with far larger data sets than Spark. <\/p>\n\n\n\n Now, let us examine the tasks each framework\nis best suited for.<\/p>\n\n\n\n Parallel Processing of\nHuge Data Sets:<\/strong> Apache Hadoop MapReduce processes large data sets in\nparallel for analysis across a Hadoop cluster. It breaks large data sets into\nsmall chunks to be processed separately on different data nodes and\nautomatically collects the analysis from different data nodes and returns as a\nsingle result. In instances of data sets being larger than available RAM,\nHadoop MapReduce may outshine Spark. <\/p>\n\n\n\n MapReduce is a good solution if the\nprocessing speed is not critical to the application. For example, if data\nprocessing can be carried out during the night, it would be logical to consider\nusing Hadoop MapReduce. <\/p>\n\n\n\n The choice of data framework should be based on the business needs in hand. Parallel processing of huge datasets is the advantage offered by Hadoop MapReduce. On the other hand, Spark boasts of faster performance, iterative processing, graph processing machine learning, and many more. In many cases, Spark may outdo Hadoop MapReduce. The good news is that Spark works fine with Hadoop Distributed File System, Apache Hive, etc. <\/p>\n\n\n\n it companies in dubai\u00a0<\/a>|\u00a0Search Engine Optimization UAE<\/a>\u00a0|\u00a0mobile app development companies uae\u00a0<\/a>|\u00a0Web Hosting UAE<\/a><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Choosing the most suitable one is a challenge when several big data frameworks are available in the market. The traditional approach of comparing the strength and weaknesses of each platform is to be of less help, as businesses should consider each framework with their needs in mind.<\/p>\n","protected":false},"author":18,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5016","post","type-post","status-publish","format-standard","hentry","category-others"],"yoast_head":"\nThe Major Difference Between\nHadoop MapReduce and Spark<\/strong><\/h4>\n\n\n\n
Tasks Hadoop MapReduce is\nIdeal For<\/strong><\/h4>\n\n\n\n
Cost-Effective If Speed\nProcessing Is Not Critical<\/strong><\/h4>\n\n\n\n
Tasks Hadoop Spark is\nIdeal For<\/strong><\/h4>\n\n\n\n
\n
Which Data framework is best suited?<\/strong> <\/h4>\n\n\n\n