Gone are the days when the data collected by businesses could be counted in kilobytes and gigabytes. Today, almost every organization collects data worth many terabytes. However, collecting data is pointless unless it can be processed to glean relevant and precise information from it. This is where big data analytics tools become handy.

Top 10 Best Big Data Analytics Tools

Let’s check out the top 10 best big data analytics tools for 2020:

1 – Apache Hadoop

Apache’s Hadoop ranks as the best big data analytics tools on our list for 2020. This software is modeled for clustered file systems and managing big data. It uses the MapReduce programming technique to process datasets in big data. Hadoop big data analytics tool is the open-source software designed in Java to provide cross-platform support successfully.

Best Big Data Analytics Tools - hadoop

Best Big Data Analytics Tools – hadoop


  • Hadoop’s core strength is in its Hadoop Distributed File System (HDFS) which can hold various types of data, from videos and images to XML, JSON and plain text in the same file system.
  • Hadoop is extremely useful for research and development (R & D) uses.
  • It provides fast access to stored data.
  • It is highly flexible and scalable.
  • Hadoop is in high demand and works on a cluster of computers.


Hadoop is free to use under Apache License.

2 – Cloudera Distribution for Hadoop (CDH)

Cloudera Distribution for Hadoop (CDH) is designed to deploy enterprise-standard technology. It is completely open-source software with a free platform distribution that involves Apache Hadoop, Apache Impala and Apache Spark among others.

Using CDH, business organizations can compile, process, administer, manage, find, create and distribute data without restriction.


  • Complete distribution facility
  • Hadoop cluster can be administered with Cloudera Manager
  • Easy to use
  • Lowered complicated administration
  • Higher governance and security


Cloudera Distribution for Hadoop (CDH) is completely free however if you wish to buy Hadoop cluster then its per-node cost is around US$ 1,000 to US$ 2,000 per terabyte.

3 – Cassandra

Cassandra, another product from Apache, is completely free and open-source. It is distributed with NoSQL DBMS made for managing large volumes of data from several different commodity servers to deliver high availability. Cassandra uses Cassandra Structure Language (CQL) to interact with the data.


  • Non-singular failure point
  • Swiftly manages immense data volumes
  • Storage is log-structured
  • Replication is automated
  • Linear scalability available
  • Simple ring architecture


Cassandra is a completely free big data analytics tool.

4 – Knime

Short for Konstanz Information Miner, KNIME is another best open source and free big data analytics tools. It is extremely useful for enterprise reporting, integration, research, data mining, CRM, text mining, data analytics and business intelligence. It runs perfectly well on Windows, Linux and OS X operating systems.

Best Big Data Analytics Tools - Knime

Best Big Data Analytics Tools – Knime


  • Easy ETL operations
  • Easy to integrate with other technologies and languages
  • High-end algorithm set
  • Easy to use with organized workflows
  • Extremely stable
  • Manual work gets automated
  • Easy to install


KNIME platform is completely free however if you wish to get extensions for the product to enhance its features, then it is chargeable accordingly.

5 – Datawrapper

Datawrapper is an excellent tool for data visualization and helps to generate simple, accurate and embeddable charts swiftly.


  • Device-friendly for mobiles, desktops and tablets
  • Swift
  • Interactive
  • Needs no coding
  • Excellent customization and export options
  • Collects all charts in a single location


Datawrapper is completely free however it does come with paid options to customize it.

6 – MangoDB

MangoDB is a document-centric, NoSQL database written in C, C++ and javaScript. It is a completely free and open-source and works perfectly well with popular operating systems. It uses the popular and efficient BSON format, along with Indexing, Sharding, Replication, etc.


  • Easy to learn
  • Supports different platforms and technologies
  • Reliable
  • Low cost
  • Easy to install and maintain


MangoDB is not free and its price is available on request.

7 – Lumify

Lumify is yet another one of the great open-source and completely free big data analytics tools in the market today. It is designed for big data fusion/integration, visualization and analytics.


  • Secure
  • Scalable
  • The dedicated full-time development team
  • Supports cloud-centric environments


Lumify is completely free to use.


8 – HPCC

High-Performance Computing Cluster, known as HCCP for short, is one of the best open-source and free big data analytics tools. It offers a complete big data solution over a flexible supercomputing platform. It is also known as Data Analytics Supercomputer or DAS, for short.

Best Big Data Analytics Tools - HPCC

Best Big Data Analytics Tools – HPCC


  • Based on commodity computing clusters for higher performance
  • Parallel data processing
  • Swift, strong and extremely flexible
  • An inexpensive and complete big data analytics solution


HPCC is completely free of charge.

Here is the HPCC Big Data Analytics Tool

9 – Apache Storm

Yet another excellent product from Apache, the Storm is a cross-platform, fault-tolerant, distributed-stream processing computing model. It is completely free and open-source. It is written in Java and Clojure.


  • Scalable
  • Reliable
  • Guaranteed fast processing
  • Fault-tolerant
  • Multiple-use cases


The storm is completely free to use.

10 – SAMOA

The Apache SAMOA (Scalable Advanced Massive Online Analysis) is an open-source platform designed for machine learning and big data stream mining.


  • Fast
  • Scalable
  • Simple and easy to use
  • Actual real-time streaming


SAMOA is completely free for use.

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Steve is a product-marketer and Engineer at Cloudysave who works with Cloud Management and Adoption team. Over the past years, he has collaborated with multiple teams to provide a robust and cost-effective architecture patterns to influence business and engineering decisions. His key areas of interests include Cloud Costs Management, Security and DevOps Best-Practices.