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

Top 10 Big Data Tools (Big Data Analytics Tools)

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: 

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:

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

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:

Cons:

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 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:

Cons:

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 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:

Cons:

Pricing: 

This tool is free of cost.

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

4. Cassandra

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:

Cons:

Pricing: 

This tool is free.

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

5. MongoDB

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:

Cons:

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 (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:

Cons:

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 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:

Cons: 

Pricing: 

For the latest price information, visit the page Rapidminer.

8. Tableau

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. 

Pros:

Cons:

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:

For the latest price information, visit the page Tableau

9. Qubole

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:

Cons:

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 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:

Cons: 

Pricing: 

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

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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