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The analysis of Big Data is a phenomenon that has gained considerable momentum in the past decade. The transition into the information age has made the analysis and visualization of Big Data vital to the success of any business. Data visualization tools enable researchers to gain insight into Big Data by identifying the underlying trends, relationships, and patterns existing within a data set. Business toolkits are essentially suites of commercial tools that search for text and provide analysis for a wide range of uses.
SAS, IBM SPSS Statistics are toolkits that are made of a number of tools that are useful in Big Data analysis.
All of these can be used for different purposes depending on the objective and circumstances. However, the rise of Open Source Big Data tools offers an economical approach towards the analysis of Big Data. Some of the well-established open source Big Data tools include Hadoop, MongoDB, Apache Samoa, and R.
Arguably the most established open source Big Data tool, Hadoop is renowned for its ability to process Big Data on a large scale. Hadoop’s framework is endowed with the capability of running on-premises as well as on the cloud, adding portability and accessibility to its long list of advantages over other tools currently available in the market. A significant feature and attribute of Hadoop are its low hardware requirements. By demanding very little in terms of hardware, Hadoop is not only economical but also minimalist in its approach while maintaining a great deal of efficiency. It is currently one of the most employed open source Big Data tools globally. “Its distributed files system, the Hadoop Distributed File System (HDFS), is adapted to working with huge-scale bandwidth,” explains Jason Bell, technical writer at Master Thesis Service.
This is an open source NoSQL database that is endowed with a host of features making it one of the most used open source Big Data tools in the market. Some of its key features include the ability to store any type of data, ranging from text and integers to data, boolean, strings, and arrays. It also features considerable flexibility in its configuration with the ability to be deployed from the cloud. “Because it employs multiple data centers and nodes, it allows for the partitioning of data, an essential feature of data management and manipulation,” states Michael Cox, Big Data Specialist at ConfidentWriters. MongoDB is also equipped with the ability to process data on the go, significantly cutting the costs associated with Big Data processing. Apart from its features, MongoDB boasts the ability to be compatible across platforms with different programming languages.
Wide-scale data visualization and statistical analysis are achieved through the use of R together with Julia and Python to form the JuPyteR stack. A salient example is the JupyteR notebook which is a renowned and well-established Big Data visualization tool. JupyteR notebook has the capacity to develop an analytical model source from over 9,000 comprehensive R Archive Network (CRAN) modules and algorithms. R also has the ability to adjust analyses on the go and obtain the results within no time. R has the added benefit of running within an SQL server that is either on the Windows or Linux servers. This tool also supports Hadoop and is highly portable.
The advent of Big Data has necessitated the development of tools that effectively and efficiently analyze and visualize data for the purpose of business. While there are a number of alternatives that are available in the market, few offer the option of being open source while retaining critical features. Three open source Big Data tools that are essential to any given business include Hadoop, MongoDB, and the R programming environment. All these tools offer the benefit of portability with the ability to be deployed from the cloud. Taking into consideration the potential benefits of Big Data open source tools, it is highly recommended that organizations and businesses employ Hadoop, MongoDB, and the R programming environment.