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Top Big Data Tools You Need to Know in 2024

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27th Dec, 2023
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    Top Big Data Tools You Need to Know in 2024

    In the early 1800s, as the field of statistics expanded, it included collecting and analyzing data. But it saw the first problem with the overwhelming amount of data. Throughout the 20th century, volumes of data kept growing at an unexpected speed and machines started storing information magnetically and in other ways. Then computers started doing the same.  Accessing and storing huge data volumes for analytics was going on for a long time. But ‘big data’ as a concept gained popularity in the early 2000s when Doug Laney, an industry analyst, articulated the definition of big data as the 3Vs. The Latest Big Data Statistics Reveal that the global big data analytics market is expected to earn $68 billion in revenue by 2025. No doubt companies are investing in big data and as a career, it has huge potential. Many business owners and professionals are interested in harnessing the power locked in Big Data using Hadoop often pursue Big Data and Hadoop Training. We will discuss more on this later in this article.

    What is Big Data?

    Big data is a huge collection of structured, semi-structured and unstructured data that organizations keep collecting for information, business, machine learning, predictive modeling and plenty of other applications. Big data is often denoted as three V’s: Volume, Variety and Velocity.

    1. Volume: Refers to the massive data that organizations collect from various sources like transactions, smart devices (IoTs), videos, images, audio, social media and industrial equipment just to name a few.
    2. Velocity: Refers to the speed that data streams into businesses, especially with the growth of IoTs and the time of processing such torrential data. Smart meters like RFID tags, sensors and smart meters are helping to deal with this in almost real-time. 
    3. Variety: Refers to the professed formats of data, from structured, numeric data in traditional databases, to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.

    what is big data

    Some examples of Big Data:

    1. Data generated by social media

    statistics show that Facebook alone generates 500+terabytes of new data every day in the form of photo, video uploads, message exchanges, comments and replies.

    2. Data generated by Jet engines

    In a flight time of 30 minutes, a single jet engine can generate 10+terabytes of, and with thousands of airplanes flying across the world each day, this data volume reaches to many Petabytes (one Petabyte equals 1,000 Terabytes).

    Types of Big Data

    1. Structured (any data that can be stored, accessed and processed in a fixed format)

     types of big data structured

    Source - Guru99.com

    2. Unstructured (Any data with an unknown form or structure, like heterogeneous data source containing a combination of simple text files, images, videos etc.)

    types of big data unstructured

    Source - Guru99.com

    3. Semi-structured (may contain both the forms of data like data represented in an XML file.

    types of big data semi structured

    Source - Guru99.com

    The key understandings of big data: 

    1.  It is a titanic amount of diverse information arriving in colossal volumes and with massive velocity.
    2. Can be structured (like numeric, easily formatted and stored), unstructured (free-form, less quantifiable) or semi-structured (both formed and free-form).
    3. Can be collected from public domains like social networks and websites or voluntarily gathered through questionnaires, product purchases, electronic check-ins, personal electronics and apps.
    4. Often stored in computer databases or the cloud and is analyzed using software specifically designed to handle large, complex data sets.

    Importance of Big Data

    It is not the amount of data a company possesses, but the importance and advantage of big data depend on how a company interprets and utilizes it. Because of its sheer diversity, it becomes inherently complex to handle big data; resulting in the need for systems capable of processing the different structural and semantic differences of big data.

    The more effectively a company is able to collect and handle big data the more rapidly it grows. Because big data has plenty of advantages, hence its importance cannot be denied.

    1. Cost Savings: Various big data tools like Hadoop, Spark, Apache and others help businesses by saving costs when they need to store huge data, thus identifying better and more effective ways of handling big data.
    2. Grasping of Market situations: Analyzing big data helps businesses to understand the market situation, like a product/service in demand, customer behavior and others. This way a company can produce more of such products or concentrate on augmenting such services that have market demand, and remain ahead of its competitors. For example, a computer manufacturing company can produce models or bring more innovations to products that are in high demand. Ecommerce businesses like Alibaba, Amazon use big data in a massive way.
    3. Time Saving: Various big data tools and technologies help businesses to collect data from various sources in real-time and analyze it immediately thereby helping the businesses in making fast decisions based on the insights received.
    4. Help in customer acquisition and retention: Acquiring new customers is as important as retaining them. Big data analytics helps companies to identify customer related trends and patterns, analyze customer behavior thus helping businesses to find ways to satisfy and retain customers and fetch new ones.
    5.  Social Media Tracking: Businesses use big data processing tools to understand the sentiment of people or customers, get feedback about themselves and feel the pulse of the audience.
    6. Innovation and product (or service) development: Big data analytics help businesses to develop and redevelop products or services appropriate to market demands. The tools, trends and technology in big data are enormously used by companies in the e-commerce sector like Amazon, Netflix, Spotify, LinkedIn, Swiggy and other players. Banking, healthcare and education are the sectors apart from others that take advantage of big data.
    7. To build insightful solutions and drive value for your enterprise you can acquire the knowledge and expertise of big data tools and technologies by exploring some of the best Big Data Courses.

    Best Big Data Tools You Need To Know in 2024

    As per a report from the IDC, by 2024, the revenues from big data analytics are expected to reach $274.3 billion. No doubt then, businesses around the globe put so much importance on big data and its potential for business and revenue increase. They leverage the advantage of big data by making the best use of the tools used in big data. A big data tool is software that extracts information from various complex data types and sets, and then processes these to provide meaningful insights. Traditional databases cannot process huge data hence best big data tools that manage big data easily are used by businesses. However, big data analytics and using big data tools must be learned. The good part is anyone from a non-technical background also can learn the skills from Big Data Analytics Training.

    We are discussing here the top big data tools:

    1. Apache Hadoop

    This open-source software framework processes data sets of big data with the help of the MapReduce programming model. Written in Java it provides cross-platform support. This is one of the most popular big data tools used by most Fortune 50 companies, including Amazon Web services, Hortonworks, IBM, Intel, Microsoft and Facebook among others.

     Pros:

    • Highly scalable, provides fast access to data and is useful for R&D purposes.
    • Offers a robust ecosystem suitable to meet the analytical needs of a developer.
    • Offers flexibility and faster data processing.
    • HDFS (Hadoop Distributed File System), which is its core strength, has the ability to hold all data types like video, images, JSON, XML, and plain text over the same file system.

    Cons:

    •  Gives disk space problems at times owing to its 3x data redundancy.

    Pricing: Free to use under the Apache License.

    2. CDH (Cloudera Distribution for Hadoop)

    Totally open source with a free platform distribution encompassing Apache Hadoop, Apache Spark, Apache Impala, and others for unlimited data processing.

      Pros

    • Less complex with easy implementation and administration.
    • Allows unlimited data processing with high security and governance.

     Cons: 

    • Certain UI features like charts on the CM service are complicated.
    • Recommendation of multiple installation options could be confusing.
    • Per node Licensing price is quite expensive.

    Pricing: As such CDH is a free software from Cloudera but the per-node cost is approximately $1000 to $2000 per terabyte.

    3. Apache Cassandra

    This is also an open-source free of cost software capable of handling huge volumes of data spread across numerous servers employing CQL(Cassandra Structure Language) to interact with the database. Apache Cassandra is used by many fortune 500 companies including Facebook, General Electric, Honeywell, Yahoo, Accenture and American Express among others.

     Pros: 

    • Handles huge data volume very fast without any single point of failure.
    • Offers automated replication, linear scalability.
    • Comes with Simple Ring architecture and log-structured storage.

     Cons: 

    • Lacks row-level locking feature. 
    • Clustering needs improvement. 
    • Troubleshooting and maintenance are not quite easy.

    Pricing: Free of cost. 

    4. KNIME

    KNIME (an acronym for Konstanz Information Miner) is an open-source tool that supports Linux and Windows operating systems. It is quite useful for Enterprise reporting, integration, research, CRM, data mining, data analytics, text mining, and deriving business intelligence. Many branded companies like Johnson & Johnson, Canadian Tire, Comcast etc., use it. 

     Cons: 

    • Occupies huge RAM. 
    • Needs improvement in data handling capacity.   

    Pricing: Free of cost. 

     5. Datawrapper

     Again, an open-source tool that helps in data visualization and prepares precise, simple and embeddable charts fast. The platform is used by many big brands like Twitter, Bloomberg, The Times and Fortune to name a few. 

    Pros: 

    • Device independent and works well on all types of devices – mobile, tablet or desktop. 
    • Very fast, fully responsive, interactive, allows to have all charts in one place with great export and customization options. 
    • No coding is required. 

    Cons: 

    • Nothing serious. Just offers a limited color palette. 

    Pricing: Offers both Free and pricing models. The pricing can be had from the Datawrapper site. 

    6. MongoDB

    Free and open-source tool supporting multiple operating systems, including Windows Vista (and later versions), OS X (10.7 and later versions), Linux, Solaris, and FreeBSD. This NoSQL, document-oriented database is written in  C, C++, and JavaScript. It is used by brands like Facebook, eBay, MetLife and Google, among others. 

     Pros: 

    • Reliable, low-cost, easy to learn tool. 
    • Smooth installation and maintenance along with support for multiple technologies and platforms. 

     Cons: 

    • Has limited analytics and is slow for certain use cases. 

    Pricing: Pricing for the enterprise and SMB versions is available on request. 

    7. Lumify 

    Used for big data fusion/integration, analytics and visualization, this is an open-source and free tool. Its primary features include 

    1. full-text search
    2. automatic layouts 
    3. geospatial and multimedia analysis 
    4. 2D and 3D graph visualizations 
    5. real-time collaboration among others. 

     Pros: 

    • Scalable and secure. 
    • Supports a cloud-based environment (works well with AWS). 
    • A dedicated and full-time support team is available for any help. 

    Pricing: Free of cost. 

    8. HPCC (High-Performance Computing Cluster)

    Written in C++ and a data-centric programming language knowns as ECL(Enterprise Control Language) the tool (developed by LexisNexis Risk Solutions ) offers a three sixty-degree big data solution over a massively scalable supercomputing platform and is also called DAS (Data Analytics Supercomputer). Based on a Thor architecture, this open-source tool offers a good substitute for Hadoop and some other big data platforms as well.

    Pros: 

    • Free to use, fast, powerful and highly scalable with parallel data processing feature.
    • Comprehensive and supports high-performance online query applications.

    Pricing: Totally free of cost.

    9. Storm or Apache Storm

    This Cross-platform, free, fault-tolerant and open-source tool is based on customized spouts and bolts architecture and written in Clojure and Java. Yahoo and Alibaba among others use this.

    Pros: 

    • Fast, fault-tolerant, reliable.
    • Comes with features like log processing, ETL (Extract-Transform-Load), continuous computation, real-time analytics, distributed RPC and machine learning.

     Cons: 

    • Difficult to learn and use as well. 
    • Debugging issues. 
    • Use of Native Scheduler and Nimbus create difficulties. 

    Pricing: Totally free of cost. 

    10. Apache SAMOA (Scalable Advanced Massive Online Analysis)

    This open-source platform (used for big data stream mining and machine learning) helps users to create the distributed streaming machine learning (ML) algorithms plus run them on multiple distributed stream processing engines or DSPEs. 

    Pros: 

    • Fast, scalable, free and fun to use with real-time streaming.

    Cons: 

    • Nothing serious to make mention of.

    Pricing: Free of cost.

    11. Talend

    It is a free and open-source data integration platform and offers different software and services as well, suitable for big data, data integration, data management, data quality, cloud storage and enterprise application integration.

    Pros

    • Handles multiple data sources with numerous connectors under one roof to customize a solution as per the need.

    Cons

    • The interface is not easy to use. 
    • Needs improvement in community support.

    Pricing: It offers a free trial of each of its product versions.

    12. Rapidminer

    Coming with various license options (like small, medium and large proprietary editions plus a free edition allowing 1 logical processor and up to 10,000 data rows), this cross-platform tool brings a comprehensive environment for data science, machine learning and predictive analytics. 

    Pros: 

    • Open-source Java core. 
    • Integrates well with APIs and the cloud. 
    • Superb customer service and technical support. 

    Cons: 

    • Online data services should be improved. 

    Pricing: The website has the entire pricing information. 

     13. Qubole

    This is an all-inclusive big data platform managing on its own thus freeing the users from managing the platform. 

    Pros

    • Superb flexibility and scalability, easy to use and enhanced big data analytics. 

    Pricing: It uses a proprietary license offering business (free of cost till 5 users) and subscription-based enterprise edition (for this the team needs to be contacted). 

    14. Tableau

    This quite famous software comes with three different options a)Tableau Desktop (for the analyst), b) Tableau Server (for enterprise use ) and Tableau Online (cloud-based). Tableau Reader and Tableau Public are the two recent additions. It is used mainly for data visualization, exploration and understanding. It is capable of handling all sizes of data and is quite easy to handle by technical and non-technical user base and comes with real-time customized dashboards. 

    Pros: 

    • Offers huge flexibility to create various types of visualizations as desired and superb data blending. 
    • Comes with various high-speed smart features and no-code data queries. 
    • Mobile friendly, offers interactive and shareable dashboards. 

     Cons: 

    • Scope of improvement areas include formatting controls and a built-in tool for deployment and migration between other tableau environments/servers. 

    Pricing: It is not free and the pricing starts from $35/month (with different desktop, server and online editions).  Each edition has a free trial offer. 

    15. Adverity

    This flexible end-to-end marketing analytics platform is very useful to marketers for effortless tracking of market performance and to uncover new insights in real-time with powerful data visualizations and AI-powered predictive analytics and others. 

     Pros 

    • Completely automated data integration ( from more than 600 data sources) with fast handling and transformation. 
    • Superb customer support with high security and governance. 
    • Strong built-in predictive analytics, high scalability, flexibility and also allows easy analysis of cross-channel performance with ROI Advisor. 

    Pricing: This software tool is not free and its subscription-based pricing can be had from the company. 

    16. Integrate.io

    This is a comprehensive elastic and scalable cloud platform tool to integrate, process, and prepare data for analytics on the cloud with low-code and no-code capabilities. It helps users to make the most of their data without having to invest in other software or hardware and also provides support via email, chats, phone and online meetings as well. 

    Pros

    • Elastic, scalable cloud-based with immediate connectivity to a variety of data stores and data transformation components. 
    • Comes with an API component required for advanced customization and flexibility. 

    Cons: 

    • No monthly billing option but only a yearly billing system is available. 

    Pricing: Quotation based, comes with a 7-days free trial option. 

    Factors Considered Selecting Best Big Data Tools

    Any business needs to customize the big data tool as per its specific requirements. Some of the important factors to look for while selecting one are: 

    1. Understanding the business objectives: Big data tools, like any other investment, should meet the current and future business demands and therefore identifying the company’s goals and making a list of the desired business outcomes is a must. The business objectives need to be broken down into quantitative analytical goals. The chosen big data tool should be able to meet the business objectives. 
    2. Factoring the cost: Before selecting any tool a thorough consideration of all costs associated is needed including memberships, growth, and additional expenditures. 
    3. Ease of use: The tools must be user-friendly, scalable and adaptable suiting a wide range of rangers (technical or non-technical). Appealing graphics will increase user interest and adoption. 
    4. Advanced Analytics: The chosen tool must be able to discover patterns in data and forecast future events going way beyond simple mathematical calculations to deliver relevant insights to build complicated forecasting algorithms.  
    5. Security: Security should be of utmost concern since the big data of a company might have sensitive information and therefore needs adequate data protection. Although the most used big data tools are quite safe with good security and governance, detailed scrutiny of this is advisable. 

    How to Choose the Right Big Data Tool?

    Choosing the right big data tool for a business will depend on the business needs of the particular business. Factors like the types of data that the business needs to manage, the information needed to be extracted from the data, the applications the business uses and the sources from where the data comes. Only after considering these a tool or even a combination of tools should be chosen. Last but not least, the cost factor also needs to be considered and accordingly, the model(s) should be chosen.

    Why Is Big Data Tool Needed?

    Having had the in-depth big data tools overview, factors to be considered before selecting a tool, here are some of the benefits that big data tools and applications offer: 

    1. Outstanding Risk Management

    Covid19 is perhaps the best example of how companies and even governments worldwide were benefited by using big data insights to predict risk and remain ready for the unexpected. 

    2. Improved Customer Service

    A Gartner survey 2020 reports that businesses, especially the growing organizations, collect customer experience data “more than non-growth companies”. Businesses of course can leverage big data to improve their customer experiences and thus increase their brand value and business both.

    Where Big Data Tools Are Used?

    Big data tools and technologies are used nowadays in almost every industrial sector across the globe to identify trends and patterns, get insights into customers’ experiences and expectations, and tackle complex problems. There is a multitude of other reasons why various companies use big data analytics. For example, reasons like augmenting research, making forecasts, making the best use of advertising by targeting the key audience, their choices, behavior patterns, psychology and more. Below is the list of business sectors where big data tool kits are used massively. 

    1. Healthcare

    Pharmaceutical companies, hospitals and research centers use big data of patient and population data for advancing healthcare, researching diseases, developing new drugs, and drawing insights on health patterns of different populations. 

    2. Finance

    The banking and financial sectors use big data for various purposes like risk assessment and management, predictive analysis, fraud detection, credit rankings, brokerage services, blockchain, and cyber security among various other purposes. 

    3. Entertainment and Media

    News channels, newspapers and media companies analyze audience data on viewing, reading, watching and listening habits. For example, Netflix, YouTube, Hulu etc. are some of the entertainment companies that provide viewing recommendations to audiences by gaining insight from big data analytics.

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    Conclusion

    Hopefully, this in-depth article covering almost everything about big data, its use, popular big data tools, both open source and paid versions, their industrial applications and the tips to choose the correct one has helped you provide an overview on the subject. Before we end, in case of going for a paid version of any big data processing tools it is advisable always to first explore and experience the trial version alongside connecting with its existing customers, if possible, to understand their feedback.

    Frequently Asked Questions (FAQs)

    1What are the 3 types of big data?

    Structured, unstructured and semi structured. 

    2What is big data example?

    Data generated by social media 

    3What is the future of big data?

    Unified, flexibility and accessibility 

    4What are best big data technologies?

    Integrate.io, Tableau, Rapidminer,Apache Hadoop and others. Actually, it depends on the exact business objective and requirements. 

    5Which is the most used big data tool?

    There are many and often used by fortune500 companies depending on their business objectives. Apache Hadoop, Tableau, Adverity are a few examples.

    Profile

    Mounika Narang

    Author

    Mounika Narang is a project manager having a specialisation in IT project management and Instructional Design. She has an experience of 10 years 
    working with Fortune 500 companies to solve their most important development challenges. She lives in Bangalore with her family.

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