R Hadoop Training Pune
The volume of data that enterprises acquire every day is increasing exponentially. It is now possible to store these vast amounts of information on low cost platforms such as Hadoop.
- Hadoop streaming
- Allowing you to perform in-depth analysis of large as well as small data.
- Allowing users to perform the analytics operations within R using a lower-level language. RHIPE is designed with several functions that help perform Hadoop Distribute File System (HDFS) as well as MapReduce operations using a simple R console.
- RClient: RClient is an R application that calls the JobTracker to execute the job with an indication of several MapReduce job resources such as Mapper, Reducer, input format, output format, input file, output file, and other several parameters that can handle the MapReduce jobs with RClient.
- JobTracker: A JobTracker is the master node of the Hadoop MapReduce operations for initializing and monitoring the MapReduce jobs over the Hadoop cluster.
- TaskTracker: TaskTracker is a slave node in the Hadoop cluster. It executes the MapReduce jobs as per the orders given by JobTracker, retrieve the input data chunks, and run R-specific Mapper and Reducer over it. Finally, the output will be written on the HDFS directory.
- HDFS: HDFS is a filesystem distributed over Hadoop clusters with several data nodes. It provides data services for various data operations.
If we think about a combined RHadoop system, R will take care of data analysis operations with the preliminary functions, such as data loading, exploration, analysis, and visualization, and Hadoop will take care of parallel data storage as well as computation power against distributed data.
- rhdfs is an R interface for providing the HDFS usability from the R console. As Hadoop MapReduce programs write their output on HDFS, it is very easy to access them by calling the rhdfs methods. The R programmer can easily perform read and write operations on distributed data files. Basically, rhdfs package calls the HDFS API in backend to operate data sources stored on HDFS.
- rmr is an R interface for providing Hadoop MapReduce facility inside the Renvironment. So, the R programmer needs to just divide their application logic into the map and reduce phases and submit it with the rmr methods. After that, rmr calls the Hadoop streaming MapReduce API with several job parameters as input directory, output directory, mapper, reducer, and so on, to perform the R MapReduce job over Hadoop cluster.
- rhbase is an R interface for operating the Hadoop HBase data source stored at the distributed network via a Thrift server. The rhbase package is designed with several methods for initialization and read/write and table manipulation operations.
R Hadoop Training Pune
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