Introduction

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.

We’ve been mentioning a lot about upskilling and switching to analytics to tackle this retrenchment season and this article will help you further explore the Big Data analytics tools you need to master to become a skilled data scientist companies are looking for. So, if you’re someone looking to switch to Big Data analytics and confused about the  Big Data analytics tools you should learn to make a successful jump, here’s a comprehensive list to consider.

20 Big Data Analytics Tools You Need to Know in 2021

1. Hadoop

Big Data is sort of incomplete without Hadoop and expert data scientists would know that. An open-source Big Data analytics tools, Hadoop offers massive storage for all kinds of data. With its amazing processing power and capability to handle innumerable tasks, Hadoop never allows you to ponder over hardware failure. Though you need to know Java to work with Hadoop, it’s worth every effort. Knowing Hadoop will put you ahead in the recruitment race.

Pros:

  • Hadoop’s core strength is its HDFS (Hadoop Distributed File System), which holds all types of data, video, images, JSON, XML and plain texts across the same file system.
  • Very useful for research and development purposes.
  • Offers easy data access.
  • Extremely scalable

Cons:

  • Data redundancy can often cause disk space problems.
  • For improved efficiency, I/O operations should have been optimized.

Pricing: With the Apache License, this Big Data Analytics tool is free to use.

2. Xplenty

This cloud-based Big Data Analytics tool for integrating, analyzing and preparing data brings all data sources together. Its intuitive graphical interface allows you with ETL, ELT or replication. Xplenty is a full toolkit to create low-code and no-code data pipelines. It provides solutions for marketing, distribution, and development.

Pros:

  • It is a cloud network that is elastic and scalable.
  • You can immediately access a range of data stores and a diverse collection of data transformation components.
  • By using the rich expression language of Xplenty, you can incorporate complex data preparation functions.
  • It offers a customized and flexible API component.

Cons:

  • There is no option for monthly subscription.

Pricing: It has a price model focused on subscriptions and can be tried for free for 7 days.

3. CDH (Cloudera Distribution for Hadoop)

CDH is a complete open-source Big Data Analytics tool and includes Apache Hadoop, Apache Spark, Apache Impala, and many more on its free distribution site. It enables you to acquire, store, manage, discover, model and distribute limitless data.

Pros:

  • Complete and accurate distribution.
  • The Hadoop cluster is very well managed by the Cloudera Manager.
  • Simple to deploy.
  • The administration is less complicated.
  • High security and administration

Cons:

  • Few complicated user interfaces like CM service charts.
  • Several suggested installation methods are confusing.

Pricing: Cloudera edition of CDH is a free Big Data Analytics tool. However, if you are interested in learning about the cost of the Hadoop cluster then the rate per node is between $1000 and $2000.

4. R

R is one of the most comprehensive Big Data analytics tools for statistical analysis. The software ecosystem is open-source, free, multi-paradigm, and diverse. The programming languages are C, Fortran, and R. Most extensively used by statisticians and data miners; its use cases include data processing, data manipulation, analysis, and visualization.

Pros:

  • The greatest value of R is the immensity of the ecosystem package.
  • Unparalleled Graphics and charting features.

Pricing: The shiny server and R studio IDE are free.

5. Cassandra

Apache Cassandra is free of cost Big Data analytics tools designed to handle large quantities of data across many commodity servers, offering high-availability. The open-source NoSQL DBMS uses CQL (Cassandra Structure Language) to interact with the database.

Pros:

  • There is no single failure point.
  • It manages huge data really quick.
  • It has log-structured storage and linear scalability.

Cons:

  • Extra troubleshooting and maintenance work is required.
  • It could have boosted the clustering.
  • There is no row-level locking feature.

Pricing: Its subscription starts from $49 Per node per month.

6. Knime

KNIME is an abbreviation for Konstanz Information Miner, which is an open-source Big Data Analytics tool. It is used for enterprise reporting, integration, data mining, data analytics, and business intelligence. It supports operating systems such as Linux, and Windows X.

Pros:

  • Quick to use ETL
  • It is very well integrated with other technologies and languages.
  • Rich set of algorithms.
  • Workflows are highly functional and structured.
  • A lot of manual tasks are automated.
  • There are no problems with stability.
  • Simple to configure.

Cons:

  • It covers nearly the whole of RAM.
  • Might have enabled graph database integration.

Pricing: It’s a free Big Data Analytics tool.

7. Datawrapper

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.

Pros:

  • Operates exceptionally well on any type of device – smartphone, laptop, or tablet.
  • Rapid and interactive responses.
  • Excellent export and customization options.

Cons:

  • Has limited options for color palettes.

Pricing: It offers free service.

8. MongoDB

MongoDB is a contemporary alternative to databases. It’s one of the best Big Data Analytics tools for working on data sets that vary or change frequently or the ones that are semi or unstructured. Some of the best uses of MongoDB include storage of data from mobile apps, content management systems, product catalogs, and more. Like Hadoop, you can’t get started with MongoDB instantly. You need to learn the tool from scratch and be aware of working on queries.

Pros:

  • Supports various platforms and technologies.
  • No install and maintenance hiccups.
  • Robust and cost-effective.

Cons:

  • It has a limited analytics resource.

Pricing: The SMB and corporate versions of MongoDB are paid, and their rates are available upon request.

9. Lumify

Lumify is one of the open-source Big Data Analytics tools to analyze and visualize large data. This Big Data Analytics tool’s key features include full-text search, 2-dimensional and 3-dimensional graphical viewings, automated templates, multimedia analysis, real-time project-or workplace collaboration, to name but a few.

Pros:

  • Scalable and secure
  • A dedicated full-time development team backs it.
  • Supports the cloud-based environment and works excellently with Amazon’s AWS.

Pricing: It is a free Big Data Analytics tool.

10. HPCC

HPCC is an abbreviation for High-Performance Computing Cluster. This open-source Big Data Analytics tool is a complete Big Data solution over a highly scalable supercomputing platform. HPCC is also known as DAS (Data Analytics Supercomputer) and was developed by LexisNexis Risk Solutions. Written in C++ and ECL(Enterprise Control Language), it is based on a Thor architecture that enables data parallelism, pipeline parallelism, and system parallelism.

Pros:

  • High performance due to the commodity computing clusters based architecture.
  • Enables parallel data processing.
  • Agile, robust and highly scalable.
  • Cost-effective and comprehensive

Pricing: It’s a free Big Data Analytics tool.

11. Storm

Storm is a cross-platform and open-source Big Data Analytics tool from Apache. Written in Java and Clojure, Backtype and Twitter are the developers of the storm. Several big brands like Yahoo, Alibaba, and The Weather Channel, to name a few are organizations that use Storm.

Pros:

  • There are many applications: real-time analysis, logging, ETL (Extract Transform Load), continuous computation, distributed RPC, machine learning.
  • Agile, reliable, and highly  scalable.

Cons:

  • Difficult to understand and to use.
  • Have debugging complexity.

Pricing: It’s a free Big Data Analytics tool.

12. Rapidminer

Rapidminer is a cross-platform Big Data Analytics tool that provides integrated data science, machine learning, and predictive analysis framework. 

Pros:

  • Availability of code-optional GUI.
  • Well integrated with cloud and APIs.
  • Excellent customer support and technical assistance.

Cons:

  • Improvements should be made to online data services.

Pricing: Rapidminer’s retail price begins at $2,500. Individuals are paid $2,500 a year for the small business version. You will be charged $5,000 for the medium-size company version.

13. Qubole

Qubole Data Service is a Big Data Analytics tool that administrates, learns, and optimizes its use independently. This helps the data team to focus on business performance.

Pros:

  • Highly flexible and optimized scalability.
  • Improved Big Data Analytics adoption.
  • Simple to use. 
  • Accessible worldwide in all AWS domains.

Pricing: Qubole is subject to a proprietary license offering a business and enterprise edition. The business version is free of charge and can be used by up to 5 people. The enterprise version is paid based on subscriptions. It is ideal for large businesses with many users. Its rate begins at $199/month.

14. Tableau

Tableau is a Big Data Analytics tool that offers various integrated solutions that help the world’s biggest organizations visualize and understand their data. It provides custom dashboards in real-time and can manage all the data sizes, and can be easily accessed by technical and non-technical professionals. It is one of the best Big Data Analytics tools for data visualization and exploration.

Pros:

  • Impeccable Data blending capabilities.
  • Provides a bouquet of intelligent characteristics.
  • Outstanding and quick support for connection with most of the databases.

Cons:

  • Could provide an integrated deployment and migration tool between different table servers and environments.

Pricing: For desktop, servers, and online, Tableau offers various editions. Its price begins at $35 a month. A free trial is available in any edition.

15. SAMOA

SAMOA is an abbreviation for Scalable Advanced Massive Online Analysis. It is an open-source  Big Data Analytics tool for big data stream mining and machine learning. It enables you to build ML algorithms and run them on many DSPEs( Distributed streaming learning devices (distributed stream processing engines).

Pros: 

  • Simple to use, highly scalable and fast.
  • Based on Write Once Run Anywhere (WORA) architecture.

Pricing: It’s a free Big Data Analytics tool.

16. OpenRefine

OpenRefine is an open-source Big Data Analytics tool for data management and data visualization of unstructured data, transforming, extending and improving it. It is compatible with operating systems like Windows, Linux, and macOS.

Pros:

  • Easy to explore large datasets
  • Dataset linking and extension tools enable extending the data with web services and external data.

Pricing: It’s a free Big Data Analytics tool.

17. HCatalog

It’s 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.

Pros:

  • Ensures users need not worry about where or in what format their data is stored. 
  • Displays data from RCFile format, text files, or sequence files in a tabular view.
  • Offers REST APIs so that external systems can access these tables’ metadata.

18. Elastisearch

This 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.

Pricing: It’s a free Big Data Analytics tool.

19. Drill

It’s 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.

Pricing: It’s a free Big Data Analytics tool.

20. Oozie

One of the best workflow processing systems, 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.

Conclusion

So, these were the 20 powerful tools you need to master if you are keen on switching to Big Data Analytics. If you’re unsure of how to get started with them, remember that there are online courses that will help you specialize in these  Big Data Analytics tools and become certified experts as well. With the time being right, master the tools and switch to a rewarding career today.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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