R + Hadoop: The Future of Data Analysis

Apache Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It is designed to scale up from a single server to thousands of machines, each of which offer a local computation and storage.

R is a programming language as well as software environment for statistical computing and graphics. It is widely used for data analysis, machine learning, and statistical modeling.

There are several ways to integrate R and Hadoop:

1. You can use R on top of Hadoop by using packages such as rhipe, rmr2, and rhipeScale. These packages allow you to use R to write MapReduce programs that can be executed on a Hadoop cluster.
2. You can use R to interact with data stored in Hadoop using packages such as rhdfs and rhbase. These packages allow you to read and write data to and from Hadoop’s distributed file system (HDFS) and HBase, a distributed, column-oriented database built on top of HDFS.
3. You can use Apache Spark’s R API to process data stored in Hadoop. Spark is a fast, with in-memory data processing engine that can be used for batch, streaming, and interactive analytics.

Overall, integrating R and Hadoop can help you perform data analysis and machine learning on large datasets stored in a Hadoop cluster.

Any workstation that supports R and Java installation can access Cassandra data using pure R script and conventional SQL. Working with remote Cassandra data in R is possible with the help of the RJDBC package and the CData JDBC Driver for Cassandra. 

By utilising the CData Driver, you can access your data in the well-liked, open-source R language using a driver created for industry-proven standards.

By using Microsoft R Open, which supports multi-threading, or open R linked with the BLAS/LAPACK libraries, you may match the speed advantages the driver achieves through managed code and multi-threading.