Best Big Data Analytics Tools

Best Big Data Analytics Tools

In this tutorial, we will learn about Best Data Analytic Tools. 

Big Data Analytics Tools

List of Big Data Analytics Tools

Data Analytics is the process of evaluating datasets to draw results, on the basis of data they get. Data Analytics is widespread in commercial industries, scientists, and researchers to make a more informed business decision and to verify theories, models, and hypotheses.

Below are the 10 Best Big Data Analytic Tools with their uses and restrictions, which can assist you to evaluate the data. Let us discuss them sequentially:

  • Tableau Public
  • OpenRefine
  • RapidMiner
  • Google Fusion Tables
  • NodeXL
  • Wolfram Alpha
  • Google Search Operators
  • Solver
  • Dataiku DSS

A. Tableau Public

Tableau Public is a simple and insightful tool. As it provides interesting insights through data visualization. It is a million-row limit. As it’s easy to use fares better than most of the other players in the data analytics market.

With Tableau’s visuals, you can examine a theory. Also, explore the data, and cross-check your understandings.

Uses of Tableau Public

· You can publish shared data visualizations to the web for free.

· No programming skills required.

Visualizations published to Tableau Public can be inserted into blogs. Also, web pages can be shared through email or social media. The shared content can be made available for downloads. The above points make it the best Big Data Analytics tool.

Limitations of Tableau Public

· All data is public and offers very little range for restricted access

· Limitation of data size

· Cannot be connected to R Programming.

· The only way to read is via OData sources, is Excel or text.

B. OpenRefine

Previously known as GoogleRefine, the data cleaning software. As it helps you clean up data for evaluation. OpenRefine operates on a row of data. Moreover, they have cells under columns, quite similar to relational database tables.

Uses of OpenRefine

· Cleaning messy data

· Transformation of data

· Parsing data from websites

Adding data to the dataset by bringing it from web services. For example, OpenRefine could be used for geocoding addresses to geographic coordinates.

Limitations of OpenRefine

· Open Refine is unsuitable for large datasets.

· OpenRefine does not work very well with big data


It helps you to manipulate, evaluate, and model data through visual programming. It is used to combine various components for data mining and machine learning.

Uses of KNIME

· No need to write blocks of code. Instead, you have to drop and drag connection points between activities.

· This data analysis tool supports programming languages.

In fact, analysis tools like these can be developed to run chemistry data, text mining, python, and R.

Limitation of KNIME

· Poor data visualization

D. RapidMiner

It provides machine learning procedures. And data mining including data visualization, processing, statistical modeling, and predictive analytics.

RapidMiner which is written in Java is gaining acceptance as a Big data analytics tool at a rapid pace.

Uses of RapidMiner

· It provides an integrated environment for business analytics, predictive analysis.

· Sideways with commercial and business applications, it is also used for application development.

Limitations of RapidMiner

· RapidMiner has size constraints with respect to the number of rows.

· For RapidMiner, you need more hardware resources than ODM and SAS.

F. NodeXL

NodeXL is a visualization and evaluation software of relationships and networks. It provides exact calculations. NodeXL is a free( not the pro version) and open-source network evaluation and visualization software. It is one of the best statistical tools for data evaluation. In which includes advanced network metrics. Also, access to social media network data importers, and automation.

Uses of NodeXL

This is one of the data analysis tools in Excel that helps in the following areas:

· Data Import

· Graph Visualization

· Graph Analysis

· Data Representation

NodeXL fit into Microsoft Excel 2007, 2010, 2013, and 2016. It opens as a workbook with a variety of worksheets containing the elements of a graph structure. That is like nodes and edges.

NodeXL can import various graph formats like as adjacency matrices, Pajek .net, UCINet .dl, GraphML, and edge lists.

Limitations of NodeXL

· You need to use several seeding terms for a particular problem.

· Running the data extractions at marginally different times.

G. Wolfram Alpha

Wolfram Alpha is a computational knowledge engine or answering engine founded by Stephen Wolfram.

Uses of Wolfram Alpha

· It is an add-on for Apple’s Siri

· Provides detailed responses to technical searches and solves calculus problems.

· Helps business users with information charts and graphs. And supports in creating topic overviews, commodity information, and high-level pricing history.

Limitations of Wolfram Alpha

· It can only deal with a publicly known number and facts, not with viewpoints.

· Wolfram Alpha limits the computation time for each query.

H. Google Search Operators

Google search Operators is a powerful resource that helps you filter Google results to get the most relevant and useful information.

Uses of Google Search Operators

· Faster filtering of Google search results

· It is Google’s powerful data analysis tool that can help discover new information.


The Solver is a Microsoft Office Excel add-in program. Similarly, it is available when you install Microsoft Excel or Office. The solver is a linear programming and optimization tool in excel.

This allows you to set limits. It is a developed optimization tool that helps in quick problem-solving.

Uses of Solver

· the final values found by Solver are a solution to interrelation and decision.

· It uses a variety of approaches, from nonlinear optimization. And also linear programming to evolutionary and genetic algorithms, to find solutions.

iii. Limitations of Solver

· Poor scaling is one of the areas where Excel Solver absences.

· It can affect solution time and quality.

· Solver affects the intrinsic solvability of your model.

J. Dataiku DSS

Dataiku DSS is a collective data science software platform. Similarly, it helps a team build, prototype, explore. Though, it delivers its own data products more effectively.

Uses of Dataiku DSS

Dataiku DSS Data analytic tools provide a collaborative visual interface in which they can build, click, and point or use languages like SQL.

Limitation of Dataiku DSS

· Limited visualization capabilities

· UI hurdles: Reloading of code/datasets

· Incapable to easily compile entire code into a single document/notebook

· Still, need to integrate with SPARK


As a result, we have learned Big Data Analytic Tools. Moreover, we studied these Data Analysis Tools: OpenRefine, RapidMiner, Tableau Public, Google Fusion Tables, NodeXL, Wolfram Alpha, Google Search Operators, Solver, Dataiku DSS uses, limitations along with a description.

We hope this blog on analytics tools will help you to understand Data Analytic Tools. Data Analytic Tools is a trending topic nowadays.