Learn How to Analyse Big Data in Hadoop with R Analytic Tool

Explore three packages for integrating R with Hadoop, including Hadoop streaming and a streaming API that lets you write mappers and reducers in any language, with cross-language bindings.

Learn how to install R Hadoop packages, set up Hadoop environment variables, and download and install multiple packages from tar sources.

Explore how the Hadoop distributed file system (HDFS) provides scalable, reliable storage across large clusters, storing billions of files and enabling high-throughput streaming reads with the right-once, read-many model.

Create directories in HDFS, copy a local data set into HDFS using Hadoop fs commands, and verify the data by listing and inspecting the directory contents.

Learn how rhdfs package in R connects to hdfs, initializes the interface, and performs file operations like put, copy, move, rename, delete, and read-write streaming.

Learn to use rhdfs to connect to hdfs, manage directories, transfer datasets, read data into R, and visualize regional distributions with a 3d pie chart.

Explore how rmr provides a Hadoop mapreduce interface in R, enabling writing mappers and reducers, easy deployment, and scalable big data analytics with flexible input and output.

Learn how to write MapReduce jobs using rmr-I in Hadoop with the R analytic tool, including loading libraries, setting input and output, and performing word count.

Learn how to write map and reduce jobs with rmr-II to analyze GDP data in Hadoop, loading GDP datasets, and counting countries with GDP less or greater than Apple revenue.

Explore how HBase, a distributed column-oriented database on a distributed file system, stores data in column families with versioned data and a unique row key for scalable big data.

Master the HBase shell to create and describe tables with column families, check status and version, and perform data operations using put, get, delete, scan, count, and truncate.

Explore the HBase shell workflow to create tables and column families, insert data with put, query with scan, modify schemas, and manage tables with truncate and describe commands.

Explore the introduction to Rhbase, focusing on thrift server communication via the c++ api, initialization, and core read/write table operations such as create, delete, describe, insert, get, and scan.

Learn to work with rhbase by starting the hbase server, configuring environment variables, creating a table with a column family, and inserting, querying, and deleting data.

Explore how Hadoop streaming uses any executable or script as map or reduce, wiring standard input and output, and orchestrating mapper and reducer tasks to process data line by line.

Learn how to use Hadoop streaming commands for input and output with -input and -output, and map and reduce with -mapper and -reducer, plus -inputformat and -outputformat options.

Learn how to analyze stock market data with Hadoop streaming using map and reduce scripts. Parse CSV input, extract open and close prices, compute the difference, and emit key-value results.

Analyze stock market data with Hadoop streaming by applying map and reduce to generate word counts, using open and close values, from local input to streaming output.

Learn how to monitor MapReduce jobs in Hadoop using the web UI and resource manager, check job status and history, and inspect map and reduce tasks and job id details.

Explore the Hadoop streaming R package, its APIs for streaming data, reading and writing data from the command line, and three data formats: table, key-value, and line readers.

Learn how to implement Hadoop streaming for an R package function, processing key-value input with text connections and a reader function.

Discover how Apache Hive extends Hadoop with Hive architecture, metastore, compiler, and execution engine to enable ETL operations and SQL-like queries on stored data.

Learn hive basics for big data analysis: primitive and complex data types, external tables, and HiveQL; perform joins, group by, and load data, noting hive lacks insert, update, or delete.

Learn to use hive commands in the hive shell to create databases and tables, load data, and query with select star from table in Hadoop.

Explore RHive, showing how the RHive package links the console to the hive data warehouse, executes HQL queries, and retrieves hive metadata such as databases and table names.

Get started with hive server and beeline: start hive, connect via beeline to hive server 2, and run queries against hive tables in a CDH 5.2 setup.

Learn to integrate R with Hive using HDFS by setting up the environment, initializing DFS, loading libraries, connecting to Hive, managing directories and data, and closing the connection.

Learn to connect to Hive with RHive, initialize the environment, load data, and run queries to analyze data with group by, distinct values, and counts in Hadoop using R.