Node js online training

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mso-bidi-theme-font:minor-latin">and storage.  Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure, which minimizes the risk of catastrophic system failure, even if a significant Hadoop is an open source platform that provides excellent data management provision. It is a framework that supports the processing of large data sets in a distributed computing environment. It is designed to expand from single servers to thousands of machines, each providing computation number of nodes become out of action. Hadoop is very valuable for large scale businesses basing on its proven benefits mso-bidi-theme-font:minor-latin">Hadoop provides more accurate facts and figures. Hadoop supports advanced features like data visualization and predictive analytics in order to provide and represent the useful insights in a best graphical manner. It can help to optimize the performance using a single server and handle huge volume of information. mso-bidi-theme-font:minor-latin">Hadoop is considered affordable for both enterprise and small business which makes it an attractive solution with endless potential. With the passage of time, companies and enterprises are getting closer to Hadoop. They are moving to implement big data to support the marketing and other efforts and resources. Monstercourses Online training which is instructor-led is useful for learning Big Data technology. Also, the training with hands-on projects would give a good grip on the technology. Hadoop started off with just two components i.e. HDFS and MapReduce mso-bidi-theme-font:minor-latin"> mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Visit the site for more details mso-bidi-theme-font:minor-latin;color:#0070C0">http://www.monstercourses.com/hadoop-training/ 115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin;color:#0000CC"> 115%;mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin">Contact us: mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">USA:  + line-height:115%;mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Calibri;mso-bidi-theme-font:minor-latin;color:red">1 772 777 1557 mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin"> & 14.0pt;line-height:115%;mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Calibri;mso-bidi-theme-font:minor-latin;color:red">+44 702 409 4077 115%;mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin"> mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Skype ID: MonsterCourses mso-bidi-theme-font:minor-latin"> 

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join