big data analytics tools
 “Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, 2011

What is Big Data?

Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools.

Today, there are millions of data sources that generate data at a very rapid rate. These data sources are present across the world. Some of the largest sources of data are social media platforms and networks. Let’s use Facebook as an example—it generates more than 500 terabytes of data every day. This data includes pictures, videos, messages, and more.

Data also exists in different formats, like structured data, semi-structured data, and unstructured data. For example, in a regular Excel sheet, data is classified as structured data—with a definite format. In contrast, emails fall under semi-structured, and your pictures and videos fall under unstructured data. All this data combined makes up Big Data.

What is Big Data Analytics?

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.

Data is meaningless until it turns into useful information and knowledge which can aid the management in decision making. For this purpose, we have several top big data software available in the market. This software help in storing, analyzing, reporting and doing a lot more with data.

Big Data has become an integral part of businesses today and companies are increasingly looking for people who are familiar with Big Data analytics tools. Employees are expected to be more competent in their skill sets and showcase talent and thought processes that would complement the organizations’ niche responsibilities. The so-called in-demand skills that were popular so far have been done away with and if there’s something hot today, it’s Big Data analytics.

Types of Big Data Analytics

  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Diagnostics analytics

Data needs to be refined

Like oil, data is only valuable if it is in a usable form. Just as crude oil is transformed into more useful products such as petroleum in oil refineries, raw data needs to be preprocessed before it can be used for analytics. In practice, real-world data collected by businesses for analytics may suffer from some of the following flaws:

  • The data contains inconsistent or inaccurate information.
  • The data contains missing information.
  • The data does not represent the population that it was intended to represent.
  • The data is not in a form that is ready for predictive analytics.

A 2022 prediction says – each user would create 1.7 megabytes of new data every second. Within a year, there would be 44 trillion gigabytes of data accumulated in the world. This raw data needs to be analyzed for business decision making, optimizing business performances, studying customer trends, and delivering better products and services.

Formats and Characteristics of Data

There are three main characteristics of big data which are called as 3 V’s, i.e. Volume, Variety and Velocity. Volume is a huge generation of data being produced from various sources. Variety elucidates upon the formation of data. On the other hand, Velocity is termed as the rate at which the data is being generated. Other than these three, there is also corresponds to the entire collection of Big Data from which we derive meaningful information. It is also used to refer to the inconsistencies as well as uncertainties that are present in the data.

It is imperative to know that Big Data Analytics has generally three different formats, i.e. Structured, Semi-Structured and Unstructured.

  • Structured Data: In the form of tables based on columns.
  • Unstructured Data: In the form of audio files, video files, images etc.
  • Semi-structured Data: The kind of data which lacks a proper rigid scheme and doesn’t conform to a data model.

Best Big Data Analytics Tools

Big data analytics tools are solutions that pull data from multiple sources and prepare it for visualization and analysis to discover deeper business insights into trends, patterns and associations within data. Big Data Analytics is a process that enables data scientists to make something out of the stack of big data generated. This analysis of big data is done using some tools that we reckon as big data analytics tools.

In this blog, we will be discussing the top 50 big data analytics tools (in no particular order) that are being leveraged by data scientists.

1. Hadoop

Hadoop helps in storing and analyzing data and is considered to be one of the best tools to handle huge data. It is written in Java and is an open-source framework. Right from plain text, images to videos, Hadoop stands the potential to hold it all. It is highly scalable and finds immense application in the field of R&D. MongoDB – used on datasets that change frequently

You may also like:

Top 12 Hadoop Technology Companies
Hadoop Analytics: It’s all about Query Performance
Relation Between Big Data Hadoop and Cloud Computing
What Is Hadoop, And How Does It Relate To Cloud?
Top 10 Tips for Hadoop Administration for Starters
Top Hadoop Terms You Need to Know

2. Talend

Talend is used for data integration and management. Talend is the leading open source integration software provider to data-driven enterprises. Our customers connect anywhere, at any speed. From ground to cloud and batch to streaming, data or application integration, Talend connects at big data scale, 5x faster and at 1/5th the cost. Cassandra – a distributed database used to handle chunks of data

3. Apache Spark

Apache Spark is one of the most powerful open source big data analytics tools. It is a data processing framework that can quickly possess very large data sets.

It can also distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools. Apache Spark features in-built for streaming, SQL, machine learning, and graph processing support and earns the site as the speediest and common generator for big data transformation.

You may also like: 
Top datasets to actualize machine learning and data training tutorial
How AI and Machine Learning Will Affect Machining
What Is Machine Learning and Where to Find the Best Courses?
Guide To Unsupervised Machine Learning: Use Cases 
What Are Transformer Models In Machine Learning
Difference between Machine learning and Artificial Intelligence
Machine Learning Models in Production
Big Data Industry Predictions For 2022   
Top Coolest Big Data Startups to watch in 2022

4. MongoDB

MongoDB is a free and open-source data analytics tool that is known to provide support for multiple technologies and platforms. It also supports multiple operating systems including Windows Vista and Linux. Also, MongoDB is easy to learn, reliable and economical – all at the same time.

5. Pentaho

Pentaho addresses the barriers that block your organization’s ability to get value from all your data. The platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict. Open, embeddable and extensible, Pentaho is architected to ensure that each member of your team — from developers to business users — can easily translate data into value


Apache Storm is a cross-platform, distributed stream processing, and fault-tolerant real-time computational framework. It is free and open-source. The developers of the storm include Backtype and Twitter. It is written in Clojure and Java.

Its architecture is based on customized spouts and bolts to describe sources of information and manipulations in order to permit batch, distributed processing of unbounded streams of data.  

7. Xplenty

Xplenty is known for integrating and processing data for analytics on the cloud. It boasts of an intuitive graphic interface and a cloud platform that is highly scalable and elastic. This data analytics tool doesn’t invest in hardware, software, or related personnel to transform raw data. Xplenty is extensively used in the field of marketing, sales, support, and developers.

8. Apache Cassandra

Big tech giants like Facebook, Accenture, Yahoo, etc. rely on Cassandra. This is an open-source framework that is known for managing huge data volume in the least possible time.  Two features that make Cassandra stand apart from the rest are linear scalability and the fact that this data analytic tool is free.

9. CDH (Cloudera Distribution for Hadoop)

Cloudera aims at enterprise-class deployments of that technology. It is totally open source and has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and many more.

It allows you to collect, process, administer, manage, discover, model, and distribute unlimited data.

10. Microsoft Azure

Microsoft Azure, formerly known as Windows Azure, is a public cloud computing platform handled by Microsoft. It provides a range of services that include computing, analytics, storage, and networking.

Windows Azure provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization so that they can run their big data workloads.

11. Zoho Analytics

Zoho Analytics is a BI and Data analytics software platform that helps its users to visually analyze data, create visualizations, and get a better and in-depth understanding of raw data.

It allows its users to integrate multiple data sources that may include business applications, databases, cloud drives, and more. It helps users generate dynamic, highly customizable, and actionable reports.

12. Splice Machine

Splice Machine is a scale-out SQL Rotational Database Management System (RDBMS). It has ACID transactions, in-memory analytics, and in-database machine learning, combined.

The big data analytics tools can scale from a few to thousands of nodes enabling applications at every scale.

13. Python

Right from data cleaning, data modelling, data reporting to building analysis algorithms, Python has got you covered. Python is a relatively easy tool to work on. I addition to being user-friendly, Python is known for its portability. There are numerous operating systems that Python supports and one can work on them without making any changes to the system.

14. Qlik Sense

Qlik Sense has gained recognition as one of the most reliable data visualization and data analytics tools. This tool focuses on data integration, data literacy and data analytics in order to make the best of data. Qlik Sense is trusted by thousands of companies worldwide. This data analytics tool comes up with innovative advancements every now and then.

15. Konstanz Information Miner (KNIME)

KNIME is a free and open-source data analytics tool that does everything from cleaning and gathering data to making it accessible to everyone. KNIME is known in the market for the deployment of Data Science workflows. One of the best features of this data analytics tool is that you need not have prior programming knowledge to derive insights.

16. RapidMiner

Much like KNIME, RapidMiner operates through visual programming and is capable of manipulating, analyzing and modeling data. RapidMiner makes data science teams more productive through an open source platform for data prep, machine learning, and model deployment. Its unified data science platform accelerates the building of complete analytical workflows – from data prep to machine learning to model validation to deployment – in a single environment, dramatically improving efficiency and shortening the time to value for data science projects.

17. Splunk

Splunk is a great option for a lot of different people. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits.

18. Power BI

Power BI is yet another powerful business analytics solution by Microsoft. Power BI comes in three versions – Desktop, Pro, and Premium. The desktop version is free for users; however, Pro and Premium are priced versions.

You can visualize your data connect to many data sources and share the outcomes across your organization. Automation is getting popularity substantially as businesses are getting more profits from automation and we can choose the best Automation solutions from microsoft power automate vs uipath for better performance.

Also see:
Types and Examples of NoSQL Databases
7 Useful DAX functions in Power BI you need to know
Green Cloud Computing – The Sustainable Way to Use the cloud
20 Best Free and Open Source NoSQL Databases
6 Examples of Using Big Data in Business
Anatomy of a MapReduce Job 

19. Alteryx

Alteryx is that one tool that companies can use to discover and analyze the data. Not just that – this data analytics tool helps in finding deeper insights by deploying and sharing the analytics at scale. With Alteryx in place, one can centrally manage users, workflows, data assets, etc. into the processes.

20 .Apache Kafka

Apache Kafka is a distributed streaming platform that is used for fault-tolerant storage. Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data

21. IBM Watson Analytics

IBM Watson is an AI-augmented data science solution that enables employees to harness the power of proprietary data, unlock its potential and apply insights gained from it in new ways. It offers a wide variety of customizable modules for lifecycle management, data applications, APIs and industry-focused specializations

22. OpenRefine

OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it, transforming it from one format into another, and extending it with web services and external data. OpenRefine can help you explore large data sets with ease.

23. R-Programming

What if I tell you that Project R, a GNU project, is written in R itself? It’s primarily written in C and Fortran. And a lot of its modules are written in R itself. It’s a free software programming language and software environment for statistical computing and graphics. The R language is widely used among data miners for developing statistical software and data analysis. Ease of use and extensibility has raised R’s popularity substantially in recent years.

24. Qubole

Qubole data service is an independent and all-inclusive Big data platform that manages, learns and optimizes on its own from your usage. This lets the data team concentrate on business outcomes instead of managing the platform.

Out of the many, few famous names that use Qubole include Warner music group, Adobe, and Gannett. The closest competitor to Qubole is Revulytics.

Also See:
The Importance of Big Data Analytics in Business
Best Practices for Big Data Protection
Using Big Data For Traditional Marketing Tactics
How to use big data to build your e-commerce brand
What You Need to Know About Working in Big Data

25. Tableau

Tableau is a software solution for business intelligence and analytics which present a variety of integrated products that aid the world’s largest organizations in visualizing and understanding their data.

The software contains three main products i.e.Tableau Desktop (for the analyst), Tableau Server (for the enterprise) and Tableau Online (to the cloud). Also, Tableau Reader and Tableau Public are the two more products that have been recently added.

26. Apache SAMOA

SAMOA stands for Scalable Advanced Massive Online Analysis. It is an open-source platform for big data stream mining and machine learning.

It allows you to create distributed streaming machine learning (ML) algorithms and run them on multiple DSPEs (distributed stream processing engines). Apache SAMOA’s closest alternative is BigML tool.

27. SAS Visual Analytics

SAS Visual Analytics makes it easy to analyze and share the type of powerful insights companies need into their data. This is one of the better options for anyone who needs an easy user interface and doesn’t mind paying for the convenience.

This software is great when it comes to creating visual displays and representations of your data. Businesses can use them to show their analyses in different meetings and help different departments understand how it all ties together.

28. SiSense

SiSense is a great option that is embraced by a lot of very seasoned business intelligence (BI) tool users because it has so many comprehensive features. It’s a great option for just about all of your needs.

SiSense is built with a couple of different parts. It has a web interface that is incredibly intuitive and also uses ElastiCube, which is their proprietary database for analyzing data. You have to download ElastiCube and run it on a local computer, but it’s pretty easy to use.

29. Plotly

Plotly is one of the most visually appealing data analytic tools available. It is a cloud based solution for data science and interpretation that allows you to modify, synthesize, and distribute your information graphically in a way that can be interacted with anywhere on the web.

Plotly uses Python framework to run, so it can handle analytics, visualization, and more with ease. It has tons of equipment to help you plot statistical data for easy analysis. It can also handle scientific graphing libraries. Arduino, Python, REST, Perl, R, MATLAB, and Julia are all compatible with Plotly.

30. ThoughtSpot

ThoughtSpot is one of those data analytics tools that offer next-generation search. This tool is home to a wide range of compelling features, especially on the AI-based recommendation systems front. Additionally, this feature stands the potential to leverage crowd-sourcing as well.

31. Trifacta

When it comes to data wrangling, Trifacta is one of the most sought-after data analytics tools. The features are such that it can be used by individuals, teams, and organizations. Trifacta does everything from cleaning to transforming data.

32. Lumify

Lumify is a free and open source tool for big data fusion/integration, analytics, and visualization.

Its primary features include full-text search, 2D and 3D graph visualizations, automatic layouts, link analysis between graph entities, integration with mapping systems, geospatial analysis, multimedia analysis, real-time collaboration through a set of projects or workspaces.

33. HPCC

HPCC stands for High-Performance Computing Cluster. This is a complete big data solution over a highly scalable supercomputing platform. HPCC is also referred to as DAS (Data Analytics Supercomputer). This tool was developed by LexisNexis Risk Solutions.


Datawrapper is an open-source Big Data Analytics tool for data visualization. It enables its users to produce clear, accurate, and embedded charts easily. It is broadly used in newsrooms across the world.


HCatalog is an open-source Big Data Analytics tool that allows experts to work on interactive analyses of large scale datasets. Developed by Apache, Drill was designed to scale 10,000+ servers and process in seconds petabytes of data and millions of records. It supports tons of file systems and databases such as MongoDB, HDFS, Amazon S3, Google Cloud Storage and more.


Elasticsearch is open-sourced enterprise search engine is developed on Java and released under the license of Apache. One of its best functionalities lies in supporting data discovery apps with its super-fast search capabilities.

37. Azure Databricks

Azure Databricks is a unified big data analytics platform that provides data management, machine learning and data science to businesses through integration with Apache Spark. Integrating with a host of data sources, it pulls data from a wide variety of sources, transforms and then analyzes it through visualizations. In addition to setting up ETL flows, it empowers enterprises to create data models for predictive analysis, forecasting and future planning.

38. Apache Airflow

Airflow is an open-source Python framework that allows authoring, scheduling and monitoring of complex data sourcing tasks for big data pipelines. Aligned with the DevOps mantra of “Configuration as Code,” it allows developers to orchestrate workflows and programmatically handle execution dependencies such as job retries and alerting. Through the use of Directed Acyclic Graphs (DAGs), developers can customize pipeline processes as needed by using multi-step workflows. They can run part of the workflow at any time, even when tasks are being updated in real time.

39. BIRT

The Business Intelligence and Reporting Tools (BIRT) project provides data extraction, exploration, and data processing for analysis through data visualizations and dashboards. It includes two main components — Report Designer and Runtime. With rich-text and graphics components for designing as well as deploying data visualizations, it empowers businesses to create enterprise-level reports.

40. Domo

Domo is a cloud-based business management suite that accelerates digital transformation for businesses of all sizes. It performs both micro and macro-level analysis to provide teams with in-depth insight into their business metrics as well as solve problems smarter and faster.

41. Apache DRILL

Apache Drill an open-source Big Data Analytics tool that allows experts to work on interactive analyses of large scale datasets. Developed by Apache, Drill was designed to scale 10,000+ servers and process in seconds petabytes of data and millions of records. It supports tons of file systems and databases such as MongoDB, HDFS, Amazon S3, Google Cloud Storage and more.

42. Apache OOZIE

One of the best workflow processing systems, Apache Oozie allows you to define a diverse range of jobs written or programmed across multiple languages. Moreover, this Big Data Analytics tool also links them to each other and conveniently allows users to mention dependencies.

43. Orange

Orange is open source data visualization and data analysis for novice and expert, and provides interactive workflows with a large toolbox to create interactive workflows to analyse and visualize data. Orange is packed with different visualizations, from scatter plots, bar charts, trees, to dendrograms, networks and heat maps.

44. Weka

Weka, an open source software, is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a data set or called from your own JAVA code. It is also well suited for developing new machine learning schemes, since it was fully implemented in the JAVA programming language, plus supporting several standard data mining tasks.

45. NodeXL

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

46. Gephi

Gephi is also an open-source network analysis and visualization software package written in Java on the NetBeans platform. Think of the giant friendship maps you see that represent linkedin or Facebook connections. Gelphi takes that a step further by providing exact calculations.

47. Adverity

Adverity is a flexible end-to-end marketing analytics platform that enables marketers to track marketing performance in a single view and effortlessly uncover new insights in real-time.

Thanks to automated data integration from over 600 sources, powerful data visualizations, and AI-powered predictive analytics, Adverity enables marketers to track marketing performance in a single view and effortlessly uncovers new insights in real-time.

You may also like:

Top 20 Artificial Intelligence Platforms for 2023

Top 12 Strategic Technology Trends for 2023

Difference between Machine learning and Artificial Intelligence

Artificial Intelligence: Automating Hiring Process For Businesses!

Top 5 Hidden Artificial Intelligence Technology

Artificial Intelligence: What Can We Expect Next?

48. Dataddo

Dataddo is a no-coding, cloud-based ETL platform that puts flexibility first – with a wide range of connectors and the ability to choose your own metrics and attributes, Dataddo makes creating stable data pipelines simple and fast.

Dataddo seamlessly plugs into your existing data stack, so you don’t need to add elements to your architecture that you weren’t already using, or change your basic workflows. Dataddo’s intuitive interface and quick set-up lets you focus on integrating your data, rather than wasting time learning how to use yet another platform.

49. Solver

Solver specializes in providing world-class financial reporting, budgeting and analysis with push-button access to all data sources that drive company-wide profitability. Solver provides BI360, which is available for cloud and on-premise deployment, focusing on four key analytics areas.

50. Skytree

Skytree is a great data analytics tool. It allows users and data scientists to create extremely accurate models very quickly. Their predictive machine learning models are extremely intuitive and make experimentation and data manipulation very easy.

Skytree offers tons of great features. Their algorithms are very scalable. This means that whether you’re a small business or entrepreneur, or a giant enterprise, you can use their models and know that they will scale to fit the size of the data that you input.

51. Google Fusion Tables

Google Fusion tables is an incredible tool for data analysis, large data-set visualization, and mapping. Not surprisingly, Google’s incredible mapping software plays a big role in pushing this tool onto the list. Take for instance this map, which I made to look at oil production platforms in the Gulf of Mexico.

52. Infogram

Infogram offers over 35 interactive charts and more than 500 maps to help you visualize your data beautifully. Create a variety of charts including column, bar, pie, or word cloud. You can even add a map to your infographic or report to really impress your audience.

These are the top Big Data analytics tools, You can comment below about the best Big data tools to add in the list.


What is Big Data Software?

Big data software is used to extract information from a large number of data sets and processing these complex data. A large amount of data is very difficult to process in traditional databases. so that’s why we can use this tool and manage our data very easily. One practical application within this context is hotel management software, offering a streamlined approach to handling data in the hospitality industry.

What are big data tools?

Big data tools refers to the various software and programming languages that are used to manage and analyze big data sets. These tools can help organizations make better decisions by allowing them to more easily identify patterns and trends in their big data.

What are the Best Big Data Tools?

Below are some of the Best Big Data Tools:







How big is the Big Data market?

The Big Data Market size was valued at USD 162.6 billion in 2021 and is projected to grow 273.4 USD billion by 2026

What is the Big Data market growth?

Big Data market is growing at a CAGR of 11.0% during 2021-2026