Top 10 Big Data Tools (Big Data Analytics Tools) in 2021

As we all well know, data is everything in today’s IT world. Moreover, this data keeps multiplying by manifolds day after day. Earlier, space was about megabytes and kilobytes, but nowadays, it is a terabyte.

Data will be valueless until it turns into useful information and knowledge, which may aid the management in a higher cognitive process. For this purpose, we’ve got several top significant data software available within the market. This software helps in storing, analyzing, reporting, and doing lots more with data.

Today almost every business is extensively flooded with big data tools and techs. They carry cost efficiency, better time management into the information-analytical tasks. In this article, you will have the top list of the best big data tools and their features but before that, let’s have some idea about Big Data. 

What is Big Data?

Big data could be a term that describes the immense volume of information – including both unstructured and structured. This data inundated a business on a day-to-day basis. But it’s not the number of important information; rather, what happens with the data is a matter of discussion—the Big data tool analyzes for insights that result in better decisions and strategic business moves.

While the term “big data” may seem comparatively new, the act of gathering and storing large amounts of knowledge for eventual analysis is ages old. The big data tool concept gained momentum during the early 2000s as the business’s mainstream because the three Vs. are Volume, Velocity, and Variety.

The use of massive Data is becoming common nowadays for businesses to outperform their peers. In most e-commerce businesses, both existing competitors and new entrants use the strategies to analyze data for competing, innovating, and growing.

Big Data helps organizations form new growth opportunities and entirely new categories of companies that will combine and analyze industry data. These enterprises store enough information about the products, services, suppliers, buyers, and customer preference to analyze the data in large numbers. 

Types of Big Data

Following are the categories of Big Data:

  1. Structured Data
  2. Unstructured Data
  3. Semi-structured Data

Now let’s know each of the data detailly. 

1. Structured Data

Any data stored, accessed, and processed within various fixed formats is termed ‘structured’ data. Over the amount of your time, engineering talent has achieved tremendous success in developing techniques for working with such reasonable data (where the format is well-known in advance) and deriving value out of it. However, these days, an issue pops up when the size of data grows mostly. The typical sizes are within a range of multiple zettabytes.  

2. Unstructured Data 

Any data with an unknown form or structure is considered unstructured data. Accept the size being large, and the unstructured data poses several challenges, such as to process value from it. A typical example of unstructured data could be a heterogeneous data source containing a mixture of straightforward text files, images, videos, etc. Now day organizations have a wealth of knowledge available with them, but unfortunately, they do not know the way to derive value out of it since this data is in its raw form or unstructured format.

3. Semi-structured Data

Semi-structured data can contain both styles of data. The semi-structured data shows itself as a structured form, but that’s not true. An example of semi-structured data could be data represented in an XML file.

Features of Big Data Tools

The features of best Big data tools are as follow: 

  • Businesses can utilize outside intelligence while making decisions.
  • It has improved customer service.
  • Immediate verification of risk posed to the servers. 
  • Better operational efficiency.

Why is the Big Data Tool Important?

The big data tool’s importance doesn’t stay limited to the proportion but how the companies use the data. Every enterprise uses data in its way; the more efficiently an organization uses its data, the more potential to grow. 

The corporate can take data from any source and analyze it to seek out answers which can enable:

  • Time Reductions: The high speed of big data tools like Hadoop and in-memory analytics identifies the latest information source that updates businesses’ analytic data. It also assists in making quick decisions. 
  • Cost Savings: Some best Big Data tools like Cloud-Based Analytics and Hadoop help bring cost advantages to business when large amounts of knowledge are stored. Not only that, these tools assist in identifying the most efficient ways of running a business. 
  • Maintain Online Reputation: The best Big data tools have the capabilities of sentimental analysis. Therefore, you’ll get feedback about who is saying what about your company. Big data tools can help if you would like to observe and improve your business’s web presence.
  • Market Conditions: By analyzing big data, you’ll get a higher understanding of current market conditions. For instance, by analyzing customers’ purchasing behaviors, a corporation can see the products that are sold the foremost and produce products in keeping with this trend. By this, it can get before its competitors.
  • Customer Acquisition and Retention: The customer is that any business’s most significant asset depends on growth. There’s no single business that may claim success without first having to ascertain a solid customer base. Even with a strong customer base, an enterprise can’t afford to disregard the stiff competition. If a business is slow to find out what customers are searching for, it’s straightforward to offer low-quality products. The employment of Big data tools allows enterprises to watch various customer-related patterns and trends. Observing customer behavior is vital to trigger loyalty.
  • Innovative and Developer: Big Data tools are a driver of innovations. Another helpful advantage of big data has the capabilities to assist companies in redeveloping their products.
  • Marketing Insights: Big data tools’ analytics helps to change the face of the business operations. This feature includes the power to match customer expectations, changing the company’s business line, and ensuring that the marketing campaigns are powerful.
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Best Examples of Big Data Tool

The best examples of big data are present in the public and personal sectors: education, targeted advertising, healthcare, manufacturing, insurance, and banking, to the tangible, real-life rundown. By the year 2021, nearly 1.7 megabytes of information will be generating every second for each person on the earth. The potential for data-driven organizational growth within the hospitality sector is gigantic.

How to choose the appropriate Big Data Tool? 

Choosing the right open source or paid big data tool will help prevent time and lessen hiccups, but this decision can’t be made blindly. Confine your mind, and there’s no “best” big data platform. Each of those programs caters to different needs, so you must choose the large data tool that best answers that most closely fits your situation. To make your choice more comfortable, we’ve compiled some standard big data tools to improve extraction, storage, cleaning, mining, visualization, analysis, and integration processes.

Top 10 Best Big Data Tools

Enlisted below are the most effective Big Data tools with their pros and cons and pricing range.

Let’s explore each data tool in detail!!

1. Apache Hadoop

Hadoop is one of the big data tools

Apache Hadoop is one of the best Big Data tool software frameworks employed for clustered classification systems and massive data handling. It processes data with the help of the MapReduce programming model. Hadoop is an open-source big data framework that’s written in Java, and it provides cross-platform support.

The key strength of Apache Hadoop is its HDFS (Hadoop Distributed File System), as it carries the flexibility for holding all types of data. Such as images, video, XML, JSON, and more. No doubt, this can be the topmost big data tool. In fact, over half the Fortune 50 companies use Hadoop. Many of the massive names include Amazon Web services, Hortonworks, IBM, Intel, Microsoft, Facebook, etc.

Pros:

  • Highly useful for R&D purposes.
  • Provides quick access to the existed data in your database.
  • Highly scalable and open source real time data processing tool.  
  • High-class service on the cluster of the computer system. 

Cons:

  • Sometimes, disc space issues will appear because of its 3x data redundancy.
  • I/O operations can improve for better performance.

Pricing: 

This open-source big data tool is liberated to use under the Apache License.

For the latest price information, visit the page Apache Hadoop.

2. Xplenty

Xplenty is one of the big data tools

Xplenty is a big data software platform for integrating, processing, and preparing data for analytics on the cloud. It’ll bring all of your data sources together. This big data tool intuitive graphic interface will help you implement ETL, ELT, or a replication solution. Xplenty may be a complete toolkit for building data pipelines with low-code and no-code capabilities. It’s solutions for marketing, sales, support, and developers.

Xplenty facilitates your business for making a detailed analysis from your existing data only without any further investment. Xplenty supports through email, chats, phone, and an internet meeting.

Pros:

  • Xplenty is a flexible and unscalable cloud platform.
  • You will get immediate connectivity to a range of knowledge stores and a chic set of data transformation components.
  • Easy implementation of elaborate data preparation with the help of Xplenty’s expression rich language. 
  • API component for advanced customization and suppleness.

Cons:

  • Only the annual billing option is accessible. The monthly subscription isn’t available.
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Pricing: 

You’ll get a quote for pricing details. It’s a subscription-based pricing model. You’ll be able to try the platform at no cost for 7-days.

For the latest price information, visit the page Xplenty.

3. Apache Storm

Apache Storm is one of the big data tools

Apache Storm an open source big data software cross-platform, distributed stream processing, and fault-tolerant real-time computational framework. It’s a free and open-source tool. The developers of the Apache storm include both Twitter and Backtype. The built-in language for apache storm is Clojure and Java.

Its architecture relies on customized spouts and bolts to explain sources of knowledge and manipulations to allow batch, distributed processing of unbounded information streams. Groupon, Alibaba, Yahoo, and The Weather Channel are many prominent organizations that use Apache Storm for data mining.

Pros:

  • Reliable at scale and open-source data processing tool.
  • Very fast and fault-tolerant.
  • Guarantees the processing of data knowledge.
  • It has multiple uses such as ETL (Extract-Transform-Load), real-time analytics, continuous computation, log processing, machine learning, and distributed RPC.

Cons:

  • It is challenging data processing tool.
  • Difficulties with debugging.
  • Use of Native Scheduler and Nimbus turns into bottlenecks.

Pricing: 

This tool is free of cost.

For the latest price information, visit the page Apache Storm.

4. Cassandra

Cassandra is one of the big data tools

Apache Cassandra is an open-source big data processing that distributes NoSQL and DBMS constructed to manage vast volumes of information spread across numerous commodity servers, delivering high availability. The device is free of any cost. It implements CQL (Cassandra Structure Language) for interacting with the database.

Most high-profile companies use Cassandra like Accenture, Facebook, American Express, Honeywell, General Electric, Yahoo, etc.

Pros:

  • No single point of failure.
  • Handles massive data very quickly.
  • Log-structured storage
  • Automated replication
  • Linear scalability
  • Simple Ring architecture

Cons:

  • Needs extra effort in troubleshooting and maintenance.
  • Clustering needs improvement.
  • The row-level locking feature isn’t there.

Pricing: 

This tool is free.

For the latest price information, visit the page, Apache Cassandra.

5. MongoDB

MongoDB is one of the big data tools

MongoDB is the best big data tool and a NoSQL, document-oriented database written in C, C++, and JavaScript. It’s liberated to use and is an open-source data tool that supports multiple operating systems like Windows Vista (and updated versions), OS X (10.7 and later versions), Linux, Solaris FreeBSD.

Its main features include MongoDB management service (MMS), Ad Hoc-queries, Aggregation, Uses BSON format, Indexing, Sharding, Replication, Server-side execution javascript, Capped collection, load balancing, and file storage. Some of the main customers using MongoDB are Facebook, MetLife, eBay, Google, etc.

Pros:

  • Easy to find out.
  • Provides support for multiple technologies and platforms.
  • No hiccups in installation and maintenance.
  • Reliable and low cost.

Cons:

  • Limited analytics.
  • Slow certainly use other cases.

Pricing: 

MongoDB’s enterprise and SMB versions are paid versions, and its pricing is accessible for the asking.

For the latest price information, visit the page MongoDB.

6. CDH  

CDH is one of the big data tools

CDH (Cloudera Distribution for Hadoop) focuses on enterprise-class deployments of that technology. This data tool is open source and incorporates a free platform distribution that encompasses Apache Spark, Apache Hadoop, Apache Impala, and many more.

CDH allows for gathering, processing, administering, managing, discovering, modeling, and distributing unlimited data.

Pros:

  • Wide distribution.
  • Cloudera Manager administers the Hadoop cluster okay.
  • Easy implementation.
  • Less complex administration.
  • High security and governance.

Cons:

  • Some complicated UI features like charts on the Cloudera management service are not available.
  • Multiple recommended approaches for installation sound confusing.

Pricing: 

CDH could be a free software version by Cloudera. However, if you’re interested in understanding the Hadoop cluster’s price, then the per-node cost is around $1000 to $2000 per terabyte.

For the latest price information, visit the page CDH.

7. Rapidminer

Rapidminer is one of the big data tools

Rapidminer is a cross-platform big data tool that offers an integrated environment for data science, machine learning, and predictive analytics. It has various licenses edition that provides small, medium, and big editions; proprietary editions as a free edition enable one logical processor and 10,000 data rows.

Organizations like Hitachi, BMW, Samsung, Airbus, etc., are the users of RapidMiner big data tools.

Pros:

  • Open-source Java core is available.
  • Easy front-line data science tools and algorithms.
  • The facility of code-optional GUI.
  • Integrates well with APIs and cloud.
  • Superb customer service and technical support.

Cons: 

  • Data services need improvement.

Pricing: 

  • Commercial Edition: $2,500 per user per year. 
  • Small Enterprise Edition: $2,500 per user per year. 
  • Medium Enterprise Edition: $5,000 per user per year. 
  • Big Enterprise Edition: $10,000 per user per year. 

For the latest price information, visit the page Rapidminer.

8. Tableau

Tableau is one of the big data tools

Tableau is the data tool software solution for business intelligence and analytics, which presents a range of integrated products that help the world’s largest organizations visualize and understand their data structure.

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

Tableau can handle all data sizes and is straightforward for inducing tech and non-technical based customer-based services. It gives you real-time customized dashboards. It’s a useful tool for data visualization and exploration. Out of the numerous companies that use Tableau are ZS Associates, Verizon Communications, and Grant Thornton. 

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Pros:

  • Great flexibility to form the kind of visualizations you wish.
  • Advanced and powerful data blending capabilities 
  • Full of smart features and razor-sharp speed.
  • Out of the box support for reference to most of the databases.
  • No-code data queries.
  • Mobile-ready, interactive and shareable dashboards.

Cons:

  • Formatting controls need improvement.
  • No built-in tool is available for deployment and migration amongst the assorted tableau servers.

Pricing: 

Tableau has different editions for desktop, server, and online. Its pricing starts from $35/month. 

Let us take a glance at the value of each edition details:

  • Tableau Desktop Personal edition: $35 per user per month + Free trial available.
  • Tableau Desktop Professional edition: $70 per user per month + Free trial available.
  • Tableau Server On-Premises or public cloud: $35 per user per month + Free trial available.
  • Tableau Online Fully Hosted: $42 per user per month + Free trial available.

For the latest price information, visit the page Tableau

9. Qubole

Qubole is one of the big data tools

Qubole is a big data tool service, an independent and all-inclusive Big data platform that manages, learns, and optimizes itself from your data usages. This lets the information team target business outcomes rather than addressing the forum.

Out of the numerous famous companies that use Qubole are Adobe, Warner music group, and Gannett. 

Pros:

  • Faster time to value.
  • Increased flexibility and scale.
  • Optimized spending.
  • Enhanced adoption of big data analysis.
  • Easy UI interface. 
  • Deletes technology lock-in.

Cons:

  • Available across the globe.

Pricing: 

Qubole has a proprietary license which offers business and enterprise editions. The business edition is freed from cost and supports up to five users. The enterprise edition is subscription-based and paid. It’s suitable for giant organizations with multiple users and uses cases. Its pricing starts from $199/mo. 

For the latest price information, visit the page Qubole.

10. R

R is one of the big data tools

R is one of the foremost comprehensive statistical analysis packages. It’s an open-source big data tool, free, multi-paradigm, and dynamic software environment. This data tool is written in C, Fortran, and R programming languages.

Statisticians and data miners broadly employ it. These data tools use data manipulation, data analysis, graphical display, and calculation.

Pros:

  • R’s most significant advantage is the abundance of data ecosystem of 
  • Unmatched charting benefits and graphics. 

Cons: 

  • Lacks in memory management and speed.
  • Not strong security.

Pricing: 

The R’s studio IDE and glossy server are free. In addition to the current, R studio offers some enterprise-ready professional products:

  • RStudio commercial desktop license: $995 per user p.a.
  • RStudio server pro commercial license: $9,995 p.a. per server + Unlimited users can use. 
  • RStudio connectivity license: $6.25 per month per user to $62 per month per user.
  • RStudio Shiny Server Pro license: $9,995 annually.

For the latest price information, visit the page RStudio.

FAQ: Know more about Big Data Tools

What do Big Data analytics tools mean?

Big data analytics tools are employed to extract information from many knowledge sets and process these complex data. A large amount of data is complicated to process in traditional databases. So that’s the reason we use big data tools for managing data efficiently. 

What language is used for the big data tools?

The reigning champs nowadays are R, Python, Scala, SAS, the Hadoop languages (Pig, Hive, etc.), and after all, Java. Eventually, a scant 12 percent of developers working with big data projects chose to use Java.

Which factors must you consider while selecting a Big Data Tool?

Consider these subsequent factors before selecting a Big Data tool…
License Cost if applicable
Quality of Customer support
Training employees in the data tool is available. 
Software requirements of the massive data Tool
Support and Update policy of the Big Data tool.
Reviews of the corporate

Is Kafka a big data tool?

Kafka is employed for real-time knowledge streams, gathering big data, or trying real-time analysis (or both). Kafka is used with in-memory microservices to supply durability, and it accustoms well to feed events to CEP (complex event streaming systems) and IoT/IFTTT-style automation systems.

Is Hadoop a big data tool?

Hadoop is an open-source distributed processing framework that is the key to stepping into the massive Data ecosystem, thus incorporating a good scope within the future. With Hadoop, one can efficiently perform advanced analytics, including predictive analytics, data processing, and machine learning applications.

Bottom Line 

Big Data has become an integral part of businesses today, and firms are increasingly searching for people accustomed to Big Data analytics tools. Employees are expected to be more competent in their skill sets and showcase talent and thought processes that will complement their niche responsibilities. The so-called in-demand skills that were popular to this point are done away with, and if there’s something hot today, it’s Big Data analytics.

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