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Top Big Data Analytics Tools Available Right Now [2024 Edition]

Big data analytics tools are like treasure maps for modern-day data adventurers. They’re the compasses and magnifying glasses that help us navigate through vast oceans of information, uncovering hidden gems of insights. Imagine them as the Sherlock Holmes of the digital era, piecing together clues from intricate data sets to reveal the bigger picture.


These tools are the wizards behind the curtain, turning raw data into valuable gold nuggets of knowledge. Whether it’s sifting through terabytes of information or predicting future trends, they’re the trusty sidekicks empowering us to decode the mysteries concealed within the data universe. They’re not just tools; they’re the magic wands that transform numbers into stories and complexities into clarity.


Table of Contents:



What is Big Data Analytics?


Big data analytics involves discovering trends, patterns, and correlations within extensive raw data sets to facilitate data-driven decision-making. This approach utilises conventional statistical analysis methods such as clustering and regression but adapts them to handle larger data volumes through modern tools and techniques.


Importance of Big Data Analytics


Big data analytics stands as a cornerstone in modern business strategies, empowering data analysts to elevate decision-making processes. By leveraging specialised software and robust structures designed for extensive data analysis, these experts drive pivotal transformations within firms.


The impact of this analytical prowess extends across various facets, magnifying marketing effectiveness, tailoring customer experiences, and optimising cost efficiency. These advantages converge into a potent strategy, offering a distinct competitive edge over industry counterparts.


The real-time capabilities of big data analytics platforms, fueled by logic and mathematical algorithms, expedite the generation of invaluable insights. This acceleration simplifies the decision-making journey, enabling swift and informed actions.


Amidst a plethora of big data analytics tools and technology saturating the market, the key lies in aligning the choice of tools with the unique needs and aspirations of each business. Selecting the right toolset becomes a pivotal step in unlocking the full potential of big data analytics for any organisation.


How is Big Data Analysed?


Big data analysis is a multi-layered process that involves gathering structured, semi-structured, and unstructured data from expansive data repositories, known as data lakes. The initial step revolves around discerning the most pertinent information aligned with the current informational requirements. This curation often employs data quality automation tools to streamline the extraction process.


Subsequently, the journey into big data analysis delves deeper, employing statistical methodologies and machine learning algorithms. These tools navigate the intricate data ecosystem, culminating in the synthesis of predictive analytics, user behaviour insights, and an array of crucial metrics. The process frequently encompasses diverse techniques such as text analytics, natural language processing, and predictive modelling to extract nuanced insights.


The ultimate goal remains the translation of these complex analytics into comprehensible and actionable end reports tailored for business users. These reports encapsulate the essence of the analysed data, presenting it in a format that enables informed decision-making within organisations.


Different Categories for Big Data Tools


Big data tools encompass various categories tailored to address distinct analytical needs within organisations. These categories serve as pillars supporting different facets of data analysis and decision-making:


  • Descriptive Analytics Tools: These tools revolve around organising historical data into easily understandable reports. They compile crucial metrics like company revenue, profit margins, sales figures, and metrics gleaned from social media platforms. Descriptive analytics tools provide a snapshot of past performance, offering insights into what has happened within the business.


  • Diagnostic Analytics Tools: Focused on uncovering the root causes behind specific issues or trends, diagnostic analytics involves techniques such as drill-down, data mining, and comprehensive data recovery. Companies employ these tools to delve deeper into data, gaining in-depth insights into the factors driving certain outcomes. By understanding ‘why’ something occurred, organisations can better strategise for the future.


  • Predictive Analytics Tools: Leveraging historical and current data, predictive analytics tools harness the power of data mining, artificial intelligence (AI), and machine learning (ML) algorithms to forecast future trends. These tools are invaluable for anticipating customer behaviour, market shifts, and potential opportunities or risks. They assist in making proactive decisions based on anticipated outcomes.Each category serves a distinct purpose, catering to different stages of the data analysis process. While descriptive analytics reflects past performance, diagnostic analytics digs into causation, and predictive analytics propels businesses towards anticipating and preparing for the future. The synergy between these categories empowers organisations to harness the full potential of big data, making informed and forward-thinking decisions.


  • Prescriptive Analytics Tools: This category prescribes specific solutions by integrating descriptive and predictive analytics. Often reliant on advanced AI and ML techniques, prescriptive analytics goes beyond identifying problems and forecasts by suggesting actionable solutions to optimise outcomes.


How Do We Compare Different Big Data Analytics Tools?

When evaluating different big data analytics tools, several key factors come into play to ensure they align with the specific needs and goals of an organisation:


  • User Interface (UI): The software’s UI should effectively manage vast and diverse data sets from multiple sources, presenting insights in an intuitive and efficient manner. Ease of navigation and comprehension within expansive data technologies is crucial for user adoption and effective utilisation.
  • Usability: Tools offering use case-specific tutorials, comprehensive training resources, and reliable technical support empower users to leverage the full functionality of the software. Ensuring that both novice users and motivated data science experts can effectively utilise the tool is essential.
  • Integrations: Seamless connectivity with a broad spectrum of data stores, ranging from common databases like Hive and Oracle to cloud platforms such as AWS, Azure, Google Cloud, and social media sources, is vital. Easy integration capabilities streamline data aggregation and processing workflows.
  • Value for Money: Scalable pricing models that align with data volume, AI capabilities, and other metrics ensure fairness, transparency, and flexibility in costs. Organisations need clear visibility into how pricing scales according to their usage to optimise cost-effectiveness.


Key Features to Assess:


  • Programming Models: A comprehensive toolset accommodating various programming models like MapReduce, Message Passing, Directed Acyclic Graph, Workflow, SQL-like, and Bulk Synchronous Parallel enhances versatility in data processing.
  • Statistical Algorithms and What-If Analysis: Robust statistical algorithms and the ability to conduct what-if analyses enable deeper insights and predictive capabilities, enhancing decision-making processes.
  • Flexible Programming Language Support: Support for a range of programming languages, such as SQL, NoSQL, Java, Python, etc., facilitates compatibility with diverse data environments and preferences.
  • Interactive Application Programming Interface (API) Software: A streamlined and interactive API simplifies integration and enhances the tool’s adaptability within complex data ecosystems.


When comparing big data analytics tools, a holistic assessment that considers these aspects, UI, usability, integrations, pricing, and key features, will enable organisations to make informed decisions aligned with their specific analytical needs and technological infrastructure. This approach ensures the chosen tool not only meets current requirements but also scales effectively as needs evolve.

Top 9 Big Data Analytics Tools and Their Features


Here is the big data analytics tools list:


  1. Tableau

    Tableau, renowned for its expansive collection of interactive dashboards in the realm of visual data analytics, offers a user-friendly experience through a drag-and-drop interface. This platform enables the customisation of visualisations with formatting tools that aid in uncovering valuable insights. Users of Tableau can seamlessly link to various data sources, facilitating statistical analyses and the creation of predictive models. Primarily valued by proficient technical users such as analysts and developers for crafting precise visual representations, Tableau’s setup can prove challenging for business users due to the requisite learning curve in acquiring the necessary skills.

    Core Features:

    • Data visualisation
    • Advanced data management (Security and scalability)
    • Embedded analytics
    • Data preparation and exploration
    • Native data connectors
    • Report sharing


  1. Python

    Python, a widely used programming language, is an essential tool for data analysts. Its hallmark is readability, distinguishing itself from more intricate languages. Given its prevalence in the tech industry, many programmers are already familiar with its syntax and structure.


    The versatility of Python is unparalleled. It boasts an extensive collection of libraries tailored to diverse data analytics tasks. For instance, NumPy and pandas excel at handling computational tasks and data manipulation, enhancing efficiency in analytical processes.


    Specialised libraries like Beautiful Soup and Scrapy facilitate web data scraping, while Matplotlib serves as a powerful tool for data visualisation and reporting. However, Python’s primary drawback lies in its speed. It consumes more memory and is comparatively slower than some other languages. Nevertheless, when creating software from the ground up, Python’s advantages far outweigh this limitation.


    Core Features:


    • Readability
    • Extensive Libraries
    • Versatility
    • Community Support
    • Ease of Learning
    • Portability
    • Integration Capabilities
    • Scalability


  1. Excel

    Excel, recognised as the world’s most renowned spreadsheet software, boasts robust functionalities suitable for data analysis. Its built-in capabilities encompass calculations and graphing functions, catering to diverse analytical needs. Regardless of your field or the complementary software you use, Excel remains a fundamental tool.


    It offers indispensable features like pivot tables, facilitating data sorting and totalling, along with tools for creating forms. Additionally, Excel streamlines data manipulation through various functions: CONCATENATE merges text, numbers, and dates into a single cell, SUMIF generates value totals based on specified criteria, and its search function simplifies data isolation.

    Core Features:

    • Spreadsheet Functionality
    • Data Analysis Tool
    • Graphing and Visualization:
    • Formula and Function Library
    • Data Management
    • Collaboration and Sharing:
    • Customisation and Automation:
    • Integration and Connectivity
    • Templates and Formatting:


  1. Chartio

    Chartio provides a business analytics platform that prioritises user-friendliness, offering both Visual SQL for business users and an SQL mode tailored for your data team, enabling the creation of dashboards. This inclusive approach ensures everyone within your organisation can harness data for informed decision-making, gaining deeper insights into trends and crafting visualisations that suit their specific requirements. Chartio excels in maintaining a balanced user experience for both categories of users, eliminating the necessity to acquire an additional analytics solution specifically for your data specialists.


    Core features:

    • Visual SQL and SQL Modes
    • Data Visualization:
    • Performance and Scalability
    • Customisation


  1. Power BI

    Power BI, developed by Microsoft, serves as an interactive data visualisation software tailored to support robust business intelligence solutions. A crucial component of the Microsoft Power Platform, Power BI encompasses a suite of applications and connectors meticulously crafted to transform a diverse range of data sources into both static and interactive visual representations.


    This tool facilitates the integration of data from various origins, spanning web pages, databases, PDFs, and structured files like spreadsheets (XLSX), XML, CSV, JSON, and SharePoint. Power BI offers cloud-based business intelligence services, known as “Power BI Services,” alongside a desktop interface termed “Power BI Desktop.” Its functionalities extend to encompass data warehouse capabilities, encompassing data mining, data preparation, and the creation of highly interactive dashboards.


    Core features:


    • Data Connectivity
    • Data Transformation and Modeling
    • Interactive Visualization
    • Dashboard Creation
    • Natural Language Querying
    • Advanced Analytics Capabilities
    • Collaboration and Sharing
    • Mobile Accessibility
    • Security and Compliance
    • Integration with Microsoft Ecosystem


  1. APACHE  Hadoop

    Hadoop is an open-source framework consisting of a distributed file system and a MapReduce engine that store and process big data respectively. Although the framework is older (launched in 2006) and slower than Spark, the fact of the matter is that many organisations that once adopted Hadoop won’t simply abandon it overnight because something better came along.


    Plus, there are upsides to Hadoop. For starters, it is tried and tested. While it is not the most user-friendly piece of software (and is inefficient at managing smaller datasets and real-time analytics), it is robust and reliable. Hadoop can be deployed on most types of commodity hardware and does not require supercomputers. Finally, because it distributes storage and workload, it’s also low-cost to run. And if that’s not enough, many enterprise cloud providers still support Hadoop. For example, IBM’s Analytics Engine


    Core features


    • Distributed File System:
    • MapReduce Processing
    • Scalability
    • Fault Tolerance
    • Versatility
    • Cost-Effectiveness
    • Support for Enterprise Ecosystems
    • Robustness and Reliability


  1. Spark

    Apache Spark stands as a software framework revolutionising data analysis and processing, catering to the needs of data analysts and scientists dealing with immense datasets. First introduced in 2012, Spark specialises in handling unstructured big data, leveraging its capability to distribute computationally intensive analytics tasks across multiple computers.


    What sets Spark apart from its counterparts, like Apache Hadoop, is its exceptional speed. Utilising RAM instead of local memory, Spark achieves speeds approximately 100 times faster than Hadoop, making it a preferred choice for projects requiring rapid data processing. It’s particularly favoured for developing complex machine learning models due to its rapidity and efficiency.


    Core features


    • Speed and Performance
    • Distributed Computing
    • Versatile Data Processing
    • Machine Learning Library (MLlib)
    • Real-Time Data Processing
    • Ease of Use
    • Fault Tolerance
    • Integration and Compatibility
    • Scalability


  1. KNIME

    KNIME, an open-source data analysis tool, enables users to leverage potent scripting languages such as R and Python to craft data science applications. With features like in-memory and multithreaded data processing, KNIME presents a user-friendly drag-and-drop GUI. This interface is intuitive for novices while serving as a sturdy platform for visual programming, streamlining data analysis and modelling effectively.


    Core features


    • Scripting Language Integration
    • In-Memory Processing: It
    • Multithreaded Data Processing:
    • User-Friendly GUI:
    • Visual Programming
    • Extensive Library of Nodes:
    • Integration with External Tools
    • Workflow Management


  1. SAS

    SAS, short for Statistical Analysis System, is a widely adopted commercial suite encompassing business intelligence and data analysis tools. Originating in the 1960s from the SAS Institute, it has continuously evolved, catering to various analytical needs. Presently, it finds extensive applications in customer profiling, reporting, data mining, and predictive modelling.


    Primarily designed for enterprise usage, SAS offers robustness, versatility, and a more straightforward interface for larger organisations, recognising the diverse levels of programming expertise within such environments.


    Core features:


    • Comprehensive Business Intelligence Suite
    • Longevity and Evolution
    • Enterprise Focus
    • Customer Profiling and Data Mining
    • Specific Modules for Varied Uses
    • Robustness and Versatility
    • Reliable Reporting Capabilities
    • Security and Compliance
    • Continuous Innovation


Wrapping Up


In the ever-evolving landscape of technology, big data analytics emerges as a pivotal force reshaping industries across the board. Its transformative power enables companies to optimise operations, attract and retain clientele, and secure a competitive advantage. Beyond the buzzwords of volume, velocity, and variety, the true essence lies in comprehending and harnessing the authenticity, variability, diversity, and visualisation inherent in big data analytics.


To start your journey of leveraging data as a strategic asset, explore the Accelerator Program in Business Analytics and Data Science at Hero Vired. Equip yourself with the skills to navigate the data-driven future and carve a successful career path in this dynamic realm.





A Big data analytic tool encompasses the techniques, software, and applications geared toward gathering, managing, and extracting valuable insights from extensive, swiftly moving data collections. These datasets originate from diverse channels like the web, mobile devices, emails, social media platforms, and interconnected smart gadgets.
The five types of big data analytics are Diagnostic Analytics, Prescriptive Analytics, Cyber Analytics, Predictive Analytics, and Descriptive Analytics.
Tableau enables individuals across the organisation to promptly address queries regarding their data, regardless of its scale. The greater the number of inquiries they pose, the greater the value extracted from the data, resulting in informed business decisions consistently enhancing operations.
Its widespread popularity stems from its rapid database creation and interaction capabilities. SQL is favoured not just for these reasons but also for its straightforward nature, handling unexpectedly intricate data analysis tasks with ease.
Hadoop stands as an open-source Java-based framework overseeing the storage and handling of extensive data volumes for various applications. By employing distributed storage and parallel processing, Hadoop efficiently manages big data and analytical tasks, dividing workloads into smaller units for simultaneous execution.

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