Data has the power of skyrocket your business to new heights. A deep analysis of data is a powerful tool to convert information into knowledge. In fact, data has become such an essential element that businesses solely rely on it to frame their next level of strategies. 

However, having data alone would do no good to your business. Big Data needs powerful tools and techniques to reveal insights from large, complex and diverse datasets. This is where the role of Big Data Analytics tools comes into the picture. Organizing buckets of loads of data into meaningful sets for deeper understanding helps the user in the decision-making process and formulating future strategies.

Public, private as well as government organizations have realized the power of Big Data to modify their strategies, based on the wants and demands of their audience.

How Does Big Data Analytics Help in Corporate World?

Companies that keep a proper track record of their past work and maintain a detailed report on client’s projects post-deployment know how useful it is to study and learn from our work. This may not be possible for all companies as some startups may not have such vast data sets. These people usually buy data from other leaders in the market to study end consumer’s behavior, sales strategy and overall progress.

A project that is well planned strategized based on reports generated from large data sets and incorporates latest trends of the industry automatically improves its chances of success.

Best Big Data Analytics Tools Available in the Market

Among an array of Big Data Analytics tools available for use, the following is a compiled list of some of the most useful ones:

1. 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.

2. MongoDB

MongoDB is yet another resource to organise 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

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

Two big names in the professional world that make use of this Big Data Analytics tool are ‘eBay’ and ‘Netflix’.

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 authorised people.

The highlighting feature of Cloudera is that it combines data from different sources into a centralised 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 visualisation 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 visualisations, 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.

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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 blog mentions only the ones that are highly popular and frequently used by organisations to manage large data sets.