Best Big Data Analytics Tools

Read on to learn about the best Big Data Analytics tools on the market today.

The ever-increasing use of technology has given rise to a stellar rate of data being generated every single second. As per official reports, every single minute on the Internet has over 400,000 hours of video streaming on Netflix and 45 million messages shared through Whatsapp.

While we all know data is the new fuel in this digital era, what happens to all this Data? Where do you think the entirety of this data is stored? Conventional models of storing and tackling data don’t work anymore. It’s wholly unfeasible and out of the league.

This is where the Big Data analytics tools come into play.

  • What is this Big Data Analytics?
  • What exactly are these Big Data tools?
  • How are they going to resolve these Data issues?
  • Are these tools beneficial for Data analysis in any way?

Let’s find out.

What is Big Data Analytics?

Big data analytics is collecting, organizing, and analyzing large data sets that may be structured, semi-structured or unstructured. 

The term “Big Data Analytics” is often used by businesses that collect customer data to discover patterns for improved decision-making, business operations, and customer relations.

One of the main challenges in big data analytics is that it can be difficult to process and make sense of such large volumes of data sets.

To overcome this challenge, businesses and individuals use special software tools to filter and organize the data for better analysis.

Let’s check these tools out, shall we?


Talend is a big data analytics tool that helps organizations make better decisions by providing insights derived from large data sets.

The tool collects, cleanses, and transforms data from multiple sources, both internal and external. It then uses advanced analytics to provide users with actionable insights.


  • Eclipse-based development environment.
  • Data flow orchestration.
  • File Management without scripting.
  • Apache open source license.
  • More than 900 pre-built connectors.


Pricing for the commercial version is available upon request.


Matlab is a programming, modeling, and simulation tool developed by Mathworks.

Matlab has great mathematical support, which is essential for big data analysis. It’s also very precise, which ensures the accuracy of the results. 

It provides excellent support for already programmed functions and integrates with many daily use devices in the engineering spectrum, making it even more flexible with work.


  • In-built graphics for enhanced user experience.
  • Extensive library of mathematical functions.
  • Access data from external sources.
  • An interactive environment with a GUI.
  • Supports computer vision and deep learning.


Student suite – $99

Home – $149 (For academic or organizational use)


Tableau is a big data analytics tool that helps organizations visualize and understand their data.

It allows users to create interactive, visual representations of data that can be used to identify trends and patterns. 

Tableau also provides a variety of ways to share data, including exporting to static images or creating interactive web applications.


  • Drag-and-drop interface.
  • Pre-built templates.
  • Advanced analytics.
  • Real-time data updates.


Personal edition – $70/user/month (billed annually)

Professional edition – $70/user/month (billed annually)

Team collaboration – $70/user/month (billed annually)

Tableau Online – $70/user/month (billed annually)

Tableau Public – free


Cloudera is a well-known name in Big data analytics.

Its popularity is partly due to its ease of use; Cloudera provides users with a graphical user interface that makes it easy to perform complex analyses.

And because it runs on the Apache Hadoop platform, Cloudera integrates seamlessly with many other big data tools. 

It also offers multiple software tools that can be used in conjunction with Hadoop to provide a complete big data solution.


  • Data management.
  • Data processing.
  • Data warehousing.
  • Reporting and analytics.


Cloudera Enterprise Data Hub Edition – $12,500 per node per year

Cloudera Enterprise Flex Edition – $5000 per node per year

Cloudera Enterprise Data Hub Trial – free for 30 days


Lumify is an open-source, big data analytics platform that enables organizations to gain insights from large data sets.

The platform offers a variety of features that make it easy to work with big data, including support for multiple data sources, a user-friendly interface, and powerful tools for collaboration, search, and discovery. 


  • Web-based interface.
  • Visualization and analysis tools.
  • Collaboration tools.
  • Search and discovery tools.


Lumify is free and open source.

Apache Spark

Apache Spark is an open-source big data analytics platform that provides a unified framework for processing, querying, and analyzing large data sets.

Spark offers a variety of features that make it easy to work with big data, including support for multiple data sources, in-memory computing, and real-time stream processing.


  • A unified framework for big data processing.
  • In-memory computing.
  • Real-time stream processing.
  • Multiple data source support.


Spark is free and open source.


MongoDB is a document-oriented database that is well suited for big data applications.

It offers a robust system for organizing and querying data and a flexible framework for building applications.

MongoDB is also scalable, making it ideal for organizations with rapidly growing data sets.

MongoDB’s support for JSON and its backward compatibility with older database versions make it an attractive option for developers looking to migrate their data to a newer platform.


  • Document-oriented database.
  • Scalable.
  • JSON support.
  • Backward compatibility.
  • Horizontal scaling.
  • Indexing.
  • Aggregation.


MongoDB Enterprise Advanced – $18,000 per server per year

MongoDB Enterprise Server – $6000 per Server per year

MongoDB Enterprise Basic – $2500 per Server per year

MongoDB Atlas – starts at $0.015 per hour


Qubole is a cloud-based big data analytics tool that helps businesses to make better decisions by providing simplified insights from large and complex data sets.

It offers a scalable and cost-effective data processing and analysis platform, making it an ideal solution for businesses of all sizes.

Qubole also provides several other tools to help with data management, such as data warehousing and ETL.


  • Scalable platform.
  • Cost-effective.
  • Data processing and analysis.
  • Data warehousing.
  • ETL.
  • Multiple data source support.
  • User-friendly interface.


Qubole offers a free trial. After the trial, pricing starts at $0.25 per hour.


Alteryx is a big data analytics tool that helps organizations make better decisions by blending data from multiple sources and providing self-service data preparation capabilities.

Alteryx can handle large amounts of data quickly and easily, making it ideal for organizations that need to make faster, better decisions.

Alteryx also integrates with several other tools, making it easy to share data and insights with other team members.


  • Drag-and-drop interface.
  • Visualization tools.
  • Reporting and analytics tools.
  • Multiple integrations.


Alteryx Designer – $2000 per user per year.

Alteryx Server – $5000 per node per year.

There are two main types of big data analytics tools –

Open source

While some data analytics tools are proprietary, open-source data analytics tools allow users to inspect the source code, make modifications and enhance the software as they see fit.

These tools are managed and maintained by organizations with a specific mission in mind and are usually designed to be publicly accessible.

Open-source tools, in general, offer an expansive list of capabilities for a variety of users, including support for a wide range of data types, features for data visualization and analysis, and integrations with other software platforms.

Commercial Data Analytics Tools

Unlike open-source tools, commercial data analytics tools are usually offered as licensed software products with a price tag.

The advantage of these tools is that they offer more comprehensive support and maintenance than open-source tools.

They also tend to offer more features out of the box, as well as better integration with other business software platforms.

Benefits of big data analytics tools in business?

Leveraging Big Data isn’t a piece of cake.

Businesses need to be equipped with robust solutions and strategies in place to handle large volumes of data.

Here is how Big Data Analytics tools come to be advantageous to businesses –

Data-Driven Decision Making

With big data analytics tools, businesses can analyze trends and patterns from large data sets to make evidence-based decisions that are likely to succeed.

In addition, these tools can help businesses identify opportunities and threats they may not have been aware of before.

Fraud detection

By analyzing large sets of data, analytics tools can help businesses detect and prevent being scammed by identifying patterns of fraudulent behavior that would be difficult to spot using conventional methods.

Analytical tools can also flag unusual activity in real-time, allowing businesses to take immediate action to prevent further losses. 

Customer segmentation and targeting

By understanding the needs and desires of different customer groups, businesses can tailor their products and services to appeal to specific customers within their audience. 

Analytics tools help with making this a reality. These tools make it easier to segment the entire customer base and target specific groups with personalized messages.

It can lead to increased customer satisfaction and loyalty and higher sales and profitability in the long run.

Predictive maintenance

To avoid disruptions in the supply chain, businesses need to be proactive about maintaining their assets, whether it be physical or digital.

Predictive maintenance is a preventative maintenance strategy that uses analytics tools to collect data about equipment performance over time.

By identifying potential problems before they occur, analytics tools can help predict an upcoming business phase to avoid unplanned downtime, reduce the cost of repairs, and improve the overall efficiency of the equipment.

Operational efficiency

By analyzing large volumes of data, analytics tools can identify inefficiencies and areas for improvement within an organization.

For instance, big data analytics can track inventory levels and trends, optimize production processes, and predict future demand.

This information can be used to make more informed decisions about where to allocate resources and how to streamline operations.

Here are five must-have features for a Big Data analytics tool –

Data visualization

Data visualization allows users to see patterns and trends that are not readily apparent in raw data. It helps users to understand complex data sets by identifying patterns, trends, and outliers. 

It could take multiple forms, from simple charts and graphs to more complex visualizations such as heat maps and node-link diagrams.

Regardless of its form, data visualization can help users gain insights into their data that they would not otherwise be able to see.

Most big data analytics tools offer some form of data visualization, usually in charts, graphs, and maps.

Data wrangling

Data wrangling is the process of cleaning and preparing data for analysis.

Most big data analytics tools offer some form of data wrangling, usually in the form of an ETL (extract, transform load) tool. 

Data wrangling involves procedures like removing invalid or duplicate data, formatting data correctly, and dealing with missing values.

Big data analytics tools typically have powerful data wrangling capabilities that make it easy to work with large datasets.

Reporting and dashboards

 Reporting and dashboards are features that are used to visualize raw data.

Reports can be customized to show specific data sets, and dashboards can be used to provide an overview of all the data.

Both reporting and dashboards can identify trends, patterns, and anomalies.

Reports can also be exported to other applications for further analysis. Dashboards can be shared with other users to see the same data sets and reports.

API support

API (Application Programming Interface) is a set of rules and protocols that allows businesses to connect their data to third-party applications and databases.

It enables businesses to create a more comprehensive view of their data and to integrate it with other systems for further analysis.

API support also allows businesses to share data with partners and customers, providing valuable insights into their operations. 

Big data analytics tools usually offer API support, usually REST (Representational State Transfer) or SOAP (Simple Object Access Protocol) APIs.

If you’ve read so far, chances are you’re already well off with the fact that Big Data isn’t just a buzzword and that Big Data analysis tools are, in fact, the biggest assets marketers can have at the moment.

To leverage the true potential of Big data, having robust strategies in place with a Data analysis tool could truly take your marketing efforts over the moon.

With this article, we believe we have provided you with an in-depth picture of everything there is to know about Big Data, along with the best Data analysis tools.

Now it’s just a matter of time before you can make sense of these complex data through the right set of tools that make for a perfect fit for your requisites in Big Data Analysis.