Top 4 Big Data Analytics Tools

Regardless of the type of business, Big Data is a term that keeps getting tossed around from every corner. So, what is Big Data and why is it relevant? It refers to the collection of large data sets that are growing exponentially with time. Today it is widely considered as the new age technology that has made it possible to generate insights in real-time. It is relevant because when Big Data is analyzed, it is possible to identify patterns and insights that would have been left undiscovered in other case. This is paramount, particularly in this digital era, where companies have to be always on the lookout for ways to maintain their business edge over the others.

Did you know the worldwide market of Big Data is estimated to rise to $103 billion in 2027 from $42 billion in 2018 ? Such is the accelerating growth of Big Data that companies are rushing to leverage it in one form or other.

Factors You Should Consider Before Choosing Big Data Analytics Tools

As you know, collecting Big Data is half the process. The other half and the most critical part involves deriving meaning from the data. This is where Big Data analytics tools comes into the picture.Some of the factors that you should consider while selecting them for your organization are:

  • Visualization – The data becomes more useful when it is displayed in a graphical format
  • Data Import & Export – It is critical to get the data in and out of different tools. Also, it is important to understand the ease or difficulty required for connecting to the big data archive the analytics tools
  • Integration – Organization typically employs additional programming languages and statistical tools for conducting other kinds of custom analysis
  • Analytic Capabilities – There are various forms of analytics abilities having various  models for different kinds of analysis such as path analysis, neural networks, time series, decision trees, link analysis, market based analysis, and predictive mining
  • Collaboration – Analysis requires collaboration in most instances

Top 4 Big Data Analytics Tools

Following are the top four of the Big Data Analytics tools that deals with different market requirements:

  • Microsoft Power BI
  • Splunk
  • Cloudera
  • SAS Institute


Having started out as a log analysis platform, today Splunk enjoys a loyal user base which includes organizations that are in love with the way the platform operates and how it enables data visualization and manipulation. Organizations that are already using Splunk for log and other types of analysis will find it easy to embrace Splunk Analytics for Hadoop.

As a platform, Splunk is famous for its user friendly web based analytics capabilities and log inspection. Some of the remarkable features of this platform is its proven collaboration component and enabling users in creating and sharing analytics and graphs dashboards. Other significant feature of Splunk is its integration with other elements of Splunk platform which includes security controls and its own search process language (SPL) that additionally offers great benefit to the users.

Microsoft Power BI

The easy accessibility and use is a prime reason why Microsoft’s Power BI is a favorite with analyst firms in the arena of business intelligence.

Microsoft expanded Power BI in 2018, making its feature of easy usability extend to Big Data, thus enabling data transformation and ingest. The key feature of this platform is its Azure Data Lake Storage Gen2 integration that is compatible with Hadoop Distributed File System (HDFS) required for big data analytics that is advanced.

Organizations who are on the lookout for a simple way into Big Data Analytics can benefit by choosing Power BI. Also, it is a preferable option for those who have already conformed to Microsoft stack standards . Power BI offers users cloud based business, integrates content packs  with dashboards that are pre-built and has the ability in reporting various kinds of data monitoring and analysis. With the collaboration ability of the platform, users can share dashboard and data and also provide alerting abilities.


As compared to most other companies, Cloudera is much more closely associated with the core Hadoop Big Data open source platform when we consider the core of Big data. This is no surprise as the company has been started by the Hadoop founders itself. Moreover, the merger with its primary rival gave Cloudera an even larger foothold in Hadoop ecosystem.

A significant feature of Cloudera is the company’s core competence and in-depth knowledge in Hadoop which is carried via its portfolio like its Cloudera Enterprise platform.

Cloudera’s Big Data tools are best suited for businesses that require a full stack that consists of core Hadoop tech for developing and collecting Big Data. Cloudera enterprise will enable organizations to develop and [process predictive analytic models with the help of different integrated tools.

SAS Visual Analytics

With an extensive background in the analytics field, SAS institute also has vast knowledge in analytics, exemplified by its many products that can work well with Big Data analytics. A chief among these offerings is Visual Analytics solutions that works on the wider SAS platform for analytics.

Visual Analytics is helpful for companies and users that desire in-depth analytics tools having functionalities such as drop and drag inorder to build advanced visualizations. A notable differentiator is its extensibility for various types of data reporting requirements and business intelligence.

Additionally, its other notable features include collaboration and its capability of information sharing across various options such as Microsoft Office applications, mobile browsers, and web browsers. SaS visual Analytics can be used as a service in the cloud or on-premises.


About Vibhuthi Viswanathan

Vibhuthi is a an avid follower of the latest trends in the world of Technology. Her writing aims to engage and educate the readers on all things Tech. When she is not twirling with words and pauses at SpringPeople, she binge reads popular literature.

Posts by Vibhuthi Viswanathan

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