Different open-source data analytic tools

Data Analytics is a growing field that is becoming a significant part of any business field. Due to this popularity, there are lots of tools and software that are available for you to do analytics. Some of them are open-sourced and some of them are proprietary. But, both have their own advantages and disadvantages.

When it comes to analytic tools, open-source tools are more popular than the proprietary ones. The reasons are it doesn’t have any lock-in by the vendor, free to use, and a large community of fellow developers to help. The open-source tools also have good if not better documentation and update support than the proprietary ones.

Here are the major open-source analytic tools that you can invest to improve your career.

Apache Hadoop

It is the go-to tool for processing a large volume of data. Hadoop is a Big data framework developed by Apache Foundation. Hadoop can process data efficiently with low hardware requirements. It has its own file system called HDFS (Hadoop Distributed File System) that handles the data storage process.

The Hadoop leverages the parallel processing by using Map Reduce programming. Hadoop is usually used in conjunction with the Apache Spark. It is a high performing memory processing engine.

To write the processing program in Hadoop, you can use any languages like c++, python, java, etc. This property gives Hadoop a great flexibility for developers. Used in more than half of the fortune 500 companies, Apache Hadoop is a must-learn for aspiring data scientists.

Mongo DB

The main function of MongoDB is for data storage and not analytics. But it does have some features that make it a good choice for real-time analytics. MongoDB is a NoSQL based database storage system. It means that you store data that has no structure in it and manipulate them using queries. Most of the data that you’ll be analyzing in real-time will have no structure. So, it makes MongoDB a perfect fit.

MongoDB can also be deployed in the cloud. It offers great flexibility of configuration. The main reason why MongoDB is used real-time analytics is that it stores data in the form of JSON objects. Most of the real-time analytics are written in Java or similar language. These applications can easily convert the JSON object to Java objects. The unstructured data can be accessed quickly in MongoDB, and it makes it a good candidate for dealing with real-time analytics.

MongoDB is the most popular data storage tool used in companies like Google, Flipkart, Amazon, and Facebook where real-time analytics is a major work.

R Environment

The R environment is a suite of software and libraries that are specifically used for data analytics. You will be working with the R-studio tool that uses the R programming language for working with your data. It has support for data storage, graphical facilities for data visualization, a variety of data analytics packages, etc. Most of the popular machine learning algorithms that you wish to deploy are present as a package in the R. It makes the work of data analyst easy as you can just install and work with the algorithms.

Other advantages of using R framework are it can run in an SQL server, has support for Apache Hadoop, cross-platform, highly scalable, and easily portable. Companies like Microsoft, Google, Amazon, etc are using R for the analysis of data.


Python is not a tool but a programming language. It is currently the most popular programming language for data analytics. The popular packages in python for data analytics are Pandas, Numpy, Scikit Learn, SciPy, and Matplotlib. These packages are used in implementing machine learning algorithms seamlessly and without much work. Python also offers data visualization capabilities in the form of the Matplotlib package.

Some other advantages of learning python are its simple syntax, large developer community, cross-platform functionality, etc. Almost all of the tech companies are using python for data analysis. So, it is a good place to start for aspiring data analyst.


With analytics gaining significance day by day, there are a variety of analytic tools available for developers and analysts. It is essential to select and learn about a good tool to advance your career in data analytics.

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