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Published on September 6th, 2017 | by Guest

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The 4 Best Big Data Tools for Programmers

In today’s technically savvy generation, the term “big data” is now as pervasive as “apple pie”. Worldwide internet traffic produces more than 2.5 Exabytes each day, or enough to fill 10 million blu-ray disks. But big data is more than just quantity of data! For scientists and entrepreneurs, “big data” has become synonymous with “predictive analytics”, or the ability to uncover hidden correlations from large amounts of data, such as spotting business trends to finding new genetic markers for drug discovery. Because big data analytics is driving new innovations in many fields, it is important that programmers know which state-or-the-art big data tools are currently available.

In this article, we have compiled a list of four programming tools that are most used by today’s successful big data analytic programmers.

1. Hadoop

Apache Hadoop is an open source framework that use clusters of commodity computers to treat big data as a single distributed programming model. This is possible with the specialized file system, the HDFS or Hadoop Distributed File System, designed for large storing and flexible “schema on read” data access patterns. The Hadoop MapReduce is a core component that provides fine-tuning of distributed computing. Application areas of Apache Hadoop include marketing analytics, machine learning, image processing, and database/web search. Use by companies like Yahoo! and Facebook is a testament to its importance as a big data analytics tool.

Getting started with Hadoop tools doesn’t require expensive hardware or a cluster. With only a few virtualbox instances on Linux, the basic features of Hadoop can be explored.

2. Python and libraries

Python is a full-featured open source interpreted programming language that has gained considerable attention in the data science community. Popular features include dynamic typing and memory management, object oriented, and flexible data structures. A key feature of Python is extensible module support: either user supplied or as shared objects, compiled with the Python C-API.

For big data analytics, many open source libraries are available. Scikit-learn is a popular machine learning library, including methods for supervised and unsupervised learning and regression. Recent Deep Neural Networks can be implemented with the Theano and Tensor Flow libraries.

3. R and libraries/toolset

The R project is another open source language that is popular for big data analytics developed for Statistical computing. Similar to Python, R is a scripting language with extensible library support through user submitted packages, freely available from a public repository (accessible through a CRAN mirror. Many statistical and data analysis methods are available covering linear and nonlinear modeling, time-series analysis, classification, clustering and other machine learning tasks.

A sampling of R extension packages for big data analytics includes reshape2 (for restructuring data columns), plyr (for aggregating data), randomForest, forecast (for time-series analysis) and database connectivity with RPostgreSQL, RMYSQL, RMongo, RODBC, and RSQLite. Apart from the analysis methods, R has built-in visualization packages and can also be used in web based packages, such as Shiny and Plotly.

4. NoSQL databases

Most big data, such as that collected from social media, are unstructured and not easily modeled with relational databases (RDBMs). For such data, NoSQL database is ideal since documents or graphical relationships are more efficiently modeled and scalable across a cluster. Also, the fact that NoSQL is linearly scalable means that performance increases with the addition of more processing nodes.

There are many open source and commercial NoSQL databases available. These databases are differentiated in terms of the data model (e.g. key-value, wide column, graph, or document). Amongst the most popular free solutions available, with respect to each are Cassandra for wide-column models, MongoDB for document based models, Redis or BerkeleyDB for Key-value models, and Neo4J for graph based models.

Conclusion

Unlike ever before, data is growing faster and faster. But, tools available to programmers have never been more powerful and easily accessible to delve into big data and make some original discoveries!

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