Top 7 Tools for Big Data Analytics

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Posted: June 19, 2017

Big Data is growing with everything growing around. And it is going to go big with each minute ticking this growth multiplies with time and results in more numbers. To that effect, quite perceptibly and purposefully, knowingly or unknowingly, every business is using or referring to Big Data is some or the other way. To make this count to their success they need to put up with a complementing process of tapping and churning data and interpreting it to offer them meaningful information that helps them to generate targeted reports.

To accomplish this right, there is a need to have the right kind of system and resources wrapped in a strategically laid program that facilitates analysis – in simple words a Big Data Analytics Tool.

If you would not like to miss your piece of benefit tracking and utilizing the data related to your business, you should think about using a Big Data Analytics Tool.

To make it easier for you, we are listing top 7 of those tools. Have a look:

Hadoop

Apache Hadoop is one of the most trusted and celebrated software frameworks that effectually offers you an open-source facility to process large sets of data in a real secure manner. By using simple programming models to provide discrete and even processing of data, Hadoop extensively helps you find a solution to your ranging data processing needs – perfectly fitting in with committed roles and customized supplies that your business demands.

Storm

Storm is another reliable product from Apache, that helps you in attending real-time big data processing needs. The best part is Storm is resistant to faults to great extent and can seamlessly work with any programming language. The tool comes with a ranging capacity to deal with varied data handling options and can effectively perform data processing even when connected nodes in the cluster die or when the messages are lost. What makes it even more useful isthat it can perform tasks like distributing Remote Procedure Call and facilitating online machine learning.

Hadoop MapReduce

Originally developed by Google, Hadoop MapReduce is highly known as a programming model and software framework that allows you to seamlessly write data processing apps. You can expect the framework to swiftly and effectively process a vast amount of data working on large assemblies of computer nodes. The framework can execute two types of major functions –first of which is the map function that filters and parts out data that need to be processed. And the second one is the reduce function that performs data analysis. Since MapReduce puts up with twofold processing, it is believed to answer to the data analysis queries more efficiently.

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Cassandra 

Apache Cassandra is a highly scalable and responsive framework which never compromises with quality and performance at any given processing situation. With Cassandra, you can expect a lot of elasticity with no downtime or disruption to applications. Besides, it allows you to pick between synchronous or asynchronous replication for each update, which makes it even more performance-oriented.Developed at Facebook to power their inbox search, Cassandra is now a popular Big Data tool used by many leading brands including Twitter, eBay, Netflix, and Reddit.

OpenRefine

OpenRefine is known for its ability to resourcefully treat and transform data from one form to another. So, if your data is into a mess of a situation, OpenRefine would refine it to its ideal form and offer you with data in the state you prefer to have. Other than this it allows you with the ability to import data in various formats and deal with cells that have multiple values. The framework even lets you create instantaneous links between datasets and easily filter data with regular expressions. Apart from all these, the framework is also known for performing advanced data operations with the General Refine Expression Language – making the task of data filtration even more resourceful.

Rapidminer

Rapidminer is mostly known for its influence in the field of data science. With immersive features and defining functionality in place, the framework allows data science teams to extensively excavate information and put it to use through a unified platform – aiming at significant information results, driven and steered by data prep, machine learning, and model deployment. Besides, the framework also offers an array of functions that allows users to seamlessly do manipulation, analysis, modeling, and integration in business processes.

MongoDB

MongoDB is technically a document database (works on a documented model) offering you all the flexibility and scalability that you need in order to perform querying and indexing for your project. The Big Data Tool maps the objects in your codebase and makes data extremely easy to work with. In addition to this, it offers ad hoc queries, indexing, and real-time aggregation so that you can effectively and clinically access and analyze your data in any given situation. Further, having distributed database at its core it offers horizontal scaling and geographic distribution with high availability and ease of use.

Hope this information will help you in choosing the right Big Data Analytics tool to help you with the right solution in your periphery of need. If you would like to know more on this topic you can reach us at mail@konstantinfo.com.