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Record Nr. |
UNINA9910821771703321 |
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Autore |
Jeyaraj Rathinaraja |
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Titolo |
Big data with Hadoop MapReduce : a classroom approach / / Rathinaraja Jeyaraj, Ganeshkumar Pugalendhi, Anand Paul |
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Pubbl/distr/stampa |
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Burlington, ON, Canada ; ; Palm Bay, Florida, USA : , : Apple Academic Press, , 2020 |
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ISBN |
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1-000-39824-2 |
1-000-43908-9 |
0-429-32173-2 |
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Descrizione fisica |
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1 online resource (427 pages) |
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Disciplina |
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Soggetti |
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Big data |
File organization (Computer science) |
COMPUTERS / Database Management / General |
COMPUTERS / Information Technology |
COMPUTERS / Management Information Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Big Data -- Hadoop Framework -- Hadoop 1.2.1 Installation -- Hadoop Ecosystem -- Hadoop 2.7.0 -- Hadoop 2.7.0 Installation -- Data Science. |
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Sommario/riassunto |
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"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 |
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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."-- |
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