LEADER 03602oam 22005412 450 001 9910794017403321 005 20200526015934.0 010 $a1-000-39824-2 010 $a1-000-43908-9 010 $a0-429-32173-2 035 $a(CKB)4100000011210034 035 $a(MiAaPQ)EBC6191843 035 $a(OCoLC)1114498339 035 $a(OCoLC-P)1114498339 035 $a(FlBoTFG)9780429321733 035 $a(EXLCZ)994100000011210034 100 $a20190901d2020 uy 0 101 0 $aeng 135 $aurun||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig data with Hadoop MapReduce $ea classroom approach /$fRathinaraja Jeyaraj, Ganeshkumar Pugalendhi, Anand Paul 210 1$aBurlington, ON, Canada ;$aPalm Bay, Florida, USA :$cApple Academic Press,$d2020. 215 $a1 online resource (427 pages) 311 $a1-77188-834-2 327 $aBig Data -- Hadoop Framework -- Hadoop 1.2.1 Installation -- Hadoop Ecosystem -- Hadoop 2.7.0 -- Hadoop 2.7.0 Installation -- Data Science. 330 $a"The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to: understand what big data is and the factors that are involved, understand the inner workings of MapReduce, which is essential for certification exams, learn the MapReduce program's features along its weaknesses, set up Hadoop clusters with 100s of physical/virtual machines, create a virtual machine in AWS and set up Hadoop MapReduce, write MapReduce with Eclipse in a simple way, understand other big data processing tools and their applications, understand various job positions in data science, regardless of the user's domain and expertise level in Hadoop MapReduce, this volume will broaden their knowledge and understanding of writing MapReduce programs to process big data. The authors advise that while it is not necessary to be an expert, readers should have some minimal knowledge of working in Ubuntu, Java, and Eclipse to set up clusters and write MapReduce jobs. The authors have emphasized more on Hadoop v2 when compared to Hadoop v1, in order to meet today's trend."--$cProvided by publisher. 606 $aBig data 606 $aFile organization (Computer science) 606 $aCOMPUTERS / Database Management / General$2bisacsh 606 $aCOMPUTERS / Information Technology$2bisacsh 606 $aCOMPUTERS / Management Information Systems$2bisacsh 615 0$aBig data. 615 0$aFile organization (Computer science) 615 7$aCOMPUTERS / Database Management / General 615 7$aCOMPUTERS / Information Technology 615 7$aCOMPUTERS / Management Information Systems 676 $a004.36 700 $aJeyaraj$b Rathinaraja$01537220 702 $aPugalendhi$b Ganeshkumar 702 $aPaul$b Anand 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910794017403321 996 $aBig data with Hadoop MapReduce$93786411 997 $aUNINA