Software Tool For Handling Big Data

There are many software tool for handling big data to help in achieving these goals and help data scientists to process data for analyzing them. Many new languages, frameworks and data storage technologies have emerged that supports handling of big data.

R : is an open-source statistical computing language that provides a wide variety of statistical and graphical techniques to derive insights from the data. It has an effective data handling and storage facility and supports vector operations with a suite of operators for faster processing. It has all the features of a standard programming language and supports conditional arguments, loops, and user-defined functions. R is supported by a huge number of packages through Comprehensive R Archive Network(CRAN). It is available on Windows, Linux, and Mac platforms. It has a strong documentation for each package. It has a strong support for data munging, data mining and machine learning algorithms along with a good support for reading and writing in distributed environment, which makes it appropriate for handling big data. However, the memory management, speed, and efficiency are probably the biggest challenge faced by R. R Studio is an Integrated Development Environment that is developed for programming in R language. It is distributed for standalone Desktop machines as well as it supports client-server architecture, which can be accessed from any browser.

Python : is yet another popular programming language, which is open source and is supported by Windows, Linux and Mac platforms. It hosts thousands of packages from third-party or community contributed modules. NumPy, Scikit, and Pandas support some of the popular packages for machine learning and data mining for data preprocessing, computing and modeling. NumPy is the base package for scientific computing. It adds support for large, multi-dimensional arrays and matrices with Python. Scikit supports classification, regression, clustering, dimensionality reduction, feature selection, and preprocessing and model selection algorithms. Pandas help in data mungingand preparation for data analysis and modeling. It has strong support for graph analysis with its NetworkX library and nltk for text analytics and Natural language processing. Python is very user-friendly and great for quick and dirty analysis on a problem. It also integrates well with spark through the pyspark library.

Scala : is an object-oriented language and has an acronym for “Scalable Language”. The object and every operation in Scala is a method-call, just like any object-oriented language. It requires java virtual machine environment. Spark, an in-memory cluster computing framework is written in Scala. Scala is becoming popular programming tool for handling big data problems.

Apache Spark : is an in-memory cluster computing technology designed for fast computation, which is implemented in Scala. It uses Hadoop for storage purpose as it has its own cluster management capability. It provides built-in APIs for Java, Scala, and Python. Recently, it has also started supporting R. It comes with 80 high-level operators for interactive querying. The in-memory computation is supported with its Resilient Distributed Data(RDD) framework, which distributes the data frame into smaller chunks on different machines for faster computation. It also supports Map and Reduce for data processing. It supports SQL, data streaming, graph processing algorithms and machine learning algorithms. Though Spark can be accessed with Python, Java, and R, it has a strong support for Scala and is more stable at this point of time. It supports deep learning with sparkling water in H2O.

Apache Hive : is an open source platform that provides facilities for querying and managing large dataset residing in distributed storage (For example, HDFS). It is similar to SQL and it is called as HiveQL. It uses Map Reduce for processing the queries and also supports developers to plug in their custom mapper and reducer codes when HiveQL lacks in expressing the desired logic.

Apache Pig : is a platform that allows analysts to analyzing large data sets. It is a high-level programming language, called as Pig Latin for creating MapReduce programs that requires Hadoop for data storage. The Pig Latin code is extended with the help of User-Defined Functions that can be written in Java, Python and few other languages. It is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

Amazon Elastic Compute Cloud(EC2) : is a web service that provides compute capacity over the cloud. It gives full control of the computing resources and allows developers to run their computation in the desired computing environment. It is one of the most successful cloud computing platform. It works on the principle of the pay-as you-go model.

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