Can The R Language Handle Big Data?

Data scientists and programmers alike have been struggling to find efficient and effective methods of managing big data. Much of this relates directly to being able to manage big data in a variety of systems, development environments, and programming languages. According to many, R can undoubtedly be used to crunch big data, but there are some challenges that may arise and some techniques that need to be used.

Using Sampling to Manage Larger Data Sets

Sampling can be used within the R language to analyze large data sets without having to analyze all the data. For some applications this can be very beneficial; it isn’t always necessary to comb through all of the data, especially for patterns and behavioral analysis. For other applications, such as medical or scientific ones, all of the data may need to be used. Sampling simply pulls out some of the data and draws correlations from these smaller data sets; it’s an excellent technique for preliminary findings and business intelligence.

The Hardware Challenges of the R Language

The R language naturally stores objects in memory, and it’s very easy to see why this could be problematic for those who are managing big data. However, there are two primary solutions to this problem: either getting better hardware or sidestepping the problem entirely. Those who want to use R the way that it is meant to be used and to store objects in memory can create fast data analysis by simply having better hardware; this means having an exceptional amount of RAM with which to store incredibly large objects. Programmers can also manage their data by chunk and store it on their hard drive; this will take extra finagling but mean that the data analysis can occur on any system.

Integrating C++ For Additional Features and Memory Management

One of the major advantages to the R language is its ability to integrate with C++. Programmers who are familiar with C++ will find that the ability to pull in features from this popular language, such as memory management and additional classes, will make it easier to create complete analysis kits within the R scripting language. The R scripting language is also an easy to use and intuitive language, which makes it ideal for creating a big data analysis solution on-the-fly. There are numerous interpreters for R, ranging from commercial and proprietary to open source; all of this makes development within the language both easier and faster.

The R scripting language is one of many ways to achieve actionable results from the data that organizations today collect. By utilizing R with big data, programmers, analysts, and data scientists can improve upon their business intelligence techniques and achieve better business outcomes. If you’re interested in big data services and the R language, contact the Software Specialists today.

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