Highly popular and frequently used Tools for Big Data Analytics

Among variety of Big Data Analytics tools available the following tools for use:

1. MongoDB

MongoDB is yet another resource to organize data that is unstructured, randomly placed or dynamic in nature, i.e. subject to frequent changes. Closely associated with databases, MongoDB is majorly used to store and retrieve information from catalogues of products, mobile apps, content management systems and other applications that provide a consistent experience across multiple systems.

Some of the popular names under this source of Big Data Analytics tools can be named as follows:

• Jaspersoft
• Nucleon BI Studio
• Charito
• Pentaho
• Analytica

2. Hadoop 

Hadoop is Apache’s framework that undertakes the distributed processing of large data sets across multiple computers using simple programming models. This software library is considered as the best one when it comes to superior processing of voluminous data sets.

Scalability being its biggest advantage, it can scale up from single servers to thousands of machines and compute them for a large amount of data. Hadoop is the first preferred choice because of its capabilities to handle failure at application layer without relying on hardware.

3. Cassandra

Fault tolerance Apache database named Cassandra is known for delivering a high level of performance along with good scalability and flexibility. When a company needs a secured platform that could handle its business critical data, it can rely on this Big Data Analytics tool.

Some direct advantages of choosing Cassandra for managing large data sets is as follows:

• Decentralization
• Fault Tolerance
• High Performance
• Durability
• Scalability
• Elasticity
• Professional support

4. Cloudera

Cloudera is a highest-value machine learning based Big Data Analytics tool that unifies data as per the required multi-function analytic applications as well as in the cloud for consistent user experience for data stored elsewhere.

This SDX makes it easier for data apps to develop and deploy highly secured datasets. The data delivered by Cloudera is easy to access through shared experience across apps and authorized people.

The highlighting feature of Cloudera is that it combines data from different sources into a centralized source, which is otherwise not possible with the majority of Big Data Analytics tools.

5. Plotly

Plotly is one of its kind tools that empowers the user to build expressive, interactive charts, descriptive dashboards and share it with a group of people. Those who are looking for great dynamic visualization but do not have sufficient time and resources to undertake the same, Plotly is the best Big Data Analytics tool.

This tool is quite useful in informative graphics that present the gathered data systematically for users to understand and derive insights. Once the graphical data is ready, the user can convert it any required a format and share it with other people in a seamless manner.

6. Hive

Apache Hive is open source data warehouse software that facilitates user to manage, read and write large datasets stored in distributed storage. With access to vast amount of data, it becomes easier for the user to get the required information by running a query using HiveQL, which is a database language similar to that of SQL.

Hive being a popular Big Data Analytics tool, it can project structure onto data already present in the storage. Two components, namely command line tool and JDBC driver, are used for connecting users to Hive.

7. Bokeh

Bokeh is the most advanced visual data representation tool that enables big data analytics experts to create dashboards from available data, interactive data applications using relevant data sets and group-based data plots.

Most frequently used for creating data visualizations, experts have used this Big Data Analytics tool to structure their bundles of data into something that is easy to understand for eyes as well as the mind. As a matter of fact, visuals always speak louder than words!

8. Tableau

Tableau is yet another data visualization tool that allows organizations to leverage the power of their most significant assets: data and people. This tool helps users to see the data in a structured format for better understanding and comparison, thereby allowing them to map relevant data and get insights.

This tool enables the user to build maps, bar charts, scatter plots and other such graphical representation, that too without programming. Understand, learn and make a decision from analytics of large data set that could be fruitful in future.

9. Neo4j

Neo4j is big data analytics platform that assists users to swim in the pool of connected data. This tool offers users to build world's leading graphical database to power real-time intelligent applications. In short, Neo4j helps Big Data business to climb to the next level.

Building connections between different data drive modern intelligent applications; this tool has the power to give your business a competitive advantage. Neo4j is the name that comes to mind when business needs graphical solutions from large datasets.

10. OpenRefine

OpenRefine is a powerful Big Data Analytics tool that is known for playing around messed up data, cleaning, organizing, transforming it from one format into another and structuring it for easy retrieval. No matter how much data an organization has gathered in an unstructured format, this tool is the one-stop solution to manage it with ease.

As a bonus feature, OpenRefine is also used to link and extend the existing dataset with various web services through extended plugins.

Conclusion

Big Data Analytics tools mentioned above are not exhaustive; there are much more that can be included in this list. This article mentions only the ones that are highly popular and frequently used by organizations to manage large data sets.