31 best big data analytics tools-1

31 Best Big Data Analytics Tools For Reporting In 2024

Big data analytics tools, such as Hadoop, Spark, and Tableau, enable the processing and analysis of vast dataseets to uncover patterns, trends and insights. These tools facilitate data storage, real time processing, and interative visualization, empowering businesses to make data-driven decisions efficiently.

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Top 10 Big Data Analytics Tools List

  1. Zoho Analytics: Great for analyzing data for free.
  2. Tableau: Easy to use for analyzing big data.
  3. Splunk Enterprise: Perfect for understanding how users behave.
  4. GoodData: Ideal for quickly organizing and managing data.
  5. Qlik Sense: Excellent for analyzing data related to purchases.
  6. Qrvey: Perfect for start-up companies looking to analyze big data.
  7. Azure Databricks: Offers high-performance analytics for Microsoft Azure.
  8. SAS Visual Analytics: Provides smart visualizations for analyzing big data.
  9. Arcadia Enterprise: Tailored for analyzing data in the telecommunications industry.
  10. DNIF Security Information & Event Management (SIEM): Great for managing event logs effectively.


With a list of big data analytics tools out there, finding the right one can be a daunting task. You’re looking to harness complex data efficiently to make informed decisions but need help narrowing down your options. Don’t worry, I’ve got you covered! In this post, I’ll share my firsthand experiences with numerous big data analytics software, helping you choose the best one for your needs.

What Are Big Data Analytics Tools?

Big data analytics tools are software designed to process, analyze, and extract valuable insights from large and intricate datasets. These tools handle both structured and unstructured data, using advanced techniques such as machine learning, predictive analytics, and data mining to uncover patterns, trends, and correlations.


The advantages and applications of big data analytics tools are vast. They facilitate data-driven decision-making, enhance business intelligence, and provide deep insights into customer behavior, market trends, and operational efficiencies. They empower organizations to predict future trends, identify opportunities, and streamline processes. By utilizing big data analytics, businesses can gain a competitive edge, innovate more effectively, manage risks efficiently, and ultimately achieve growth and success.

Overview of the top 10 big data analytical tools List

Here’s a quick rundown of the top 10 big data analytics tools, along with a summary of their key features and offerings:

Zoho Analytics

Zoho Analytics

Best for Free data analytics tool

Price: Starts from $24/month after a 15-day free trial


Zoho Analytics is a self-service BI and analytics software used by major companies like Hyundai, Ikea, HP, and Philips. Their free plan allows for up to 2 users and 10K rows/records, offering access to unlimited reports and dashboards. The platform provides pre-built visualizations categorized by function (e.g., social media, finance, IT, sales) to facilitate easy data analysis.



  • Easy report and dashboard customization
  • Embedded AI feature (ZIA)
  • Report generation directly from SQL queries
  • Integration with other Zoho apps


  • Entry-level plan lacks hourly data sync
  • No auto-export to Google Drive
  • Dashboards can appear crowded and busy



Best for Ease of use in big data analytics


Price: Starts from $70 per user per month, with a 14-day free trial


Tableau stands out as an intuitive visual analytics platform designed for easy data exploration and storytelling. It offers interactive data visualization through maps and charts, making complex data easy to understand. With its user-friendly interface, Tableau provides self-service prep and analytics tools that require minimal training. Users can create visualizations using drag-and-drop features and AI-driven statistical modeling without needing advanced programming skills.



  • Seamless integration with Salesforce CRM
  • Robust mobile app available for iOS and Android devices
  • Wide range of chart types including Sankey, Doughnut, and Maps
  • Self-learning module for quick adoption



  • Some data manipulation may be needed to match queries
  • Limited space for columns in worksheets
  • Database connections may need frequent re-authentication

Splunk Enterprise

Splunk Enterprise

Best for User behavior analytics


Price: Starts from $2000 per year for 1 GB/day, with a free plan limited to 500 MB/day


Splunk Enterprise is trusted by 91 of the Fortune 100 companies, including Intel, Comcast, and Coca-Cola. It offers machine learning-centric visibility and detection capabilities for entity profiling, risk behavior detection, anomaly observation, and behavior-based alerts. Splunk provides scenario-specific reports for various purposes such as app delivery, security, executive view, and IoT. Users can explore a free cloud-based sandbox trial of Splunk UBA before making a commitment. The platform caters to DevOps, Security, IT, and big data needs.



  • Flexible data and report sharing through URL links
  • Quick log queries across different infrastructure types
  • Ability to save search queries for repeat use or conversion into apps
  • Detailed and specific alerts can be set up for various KPIs


  • Requires more manpower for infrastructure maintenance compared to some competitors
  • Query builder may be complex for non-technical users
  • Steeper learning curve compared to other tools



Best for Agile data warehousing


Price: Starts from $20 per workspace per month, with a free demo available


GoodData is a big data analytics platform offering tools, runtimes, and storage for data ingestion, preparation, transformation, and analytic queries. It features over 50 connectors for data ingestion/synchronization and provides an Agile data warehousing system on higher tier plans. With a per-workspace pricing model, GoodData allows unlimited users to access data models, metrics, calculations, and dashboards based on flexible permissions.



  • Easy dashboard and view creation for non-technical users
  • Excellent integration with Salesforce, Pardot, Zendesk
  • Allows scheduling reports according to specific times and frequencies
  • Facilitates linking of disparate data sources for comparison


  • Requires coding knowledge for inquiries and report building
  • Performance may be impacted with datasets exceeding 100M rows
  • Some data model adjustments may necessitate customer support

Qlik Sense

Qlik Sense

Best for Purchasing departments in big data analytics


Price: Starts from $30 per user per month, with a 30-day free trial


Qlik Sense is an end-to-end data analytics platform featuring an associative analytics engine. This unique engine allows users to freely search and explore data in any direction without pre-aggregated data or predefined queries. It’s particularly beneficial for purchasing departments, offering in-depth supplier and industry trend comparisons, easy currency filters for international partners, and reports on low product or low spend.



  • Thorough and quick search functionality
  • Incorporates data modeling for “no-warehouse” options
  • Easy reuse of code or query logic for time-saving
  • Low learning curve for self-service


  • Not as customizable as some competitors, like Tableau
  • Limited non-interactive report creation options
  • Low-resolution monitors may struggle to display clearly



Best for Big data analysis for start-ups


Price: Starts from $2500 per year, with a free trial available


Qrvey is an embedded analytics platform tailored for SaaS data, analytics, and automation technologies. It can be seamlessly deployed into your existing AWS account to visualize your entire data pipeline. Their start-ups package offers specialized support for pre-launch or early-launch companies, including quick installation and launch, serverless analytics scalability, no-code embedded widgets, up to 10 GB of data, and a lower entry subscription price point.



  • Good out-of-the-box workflows and automation tools
  • Ability to embed charts into your own web app without iFrames
  • Unlimited users and API calls for every plan tier


  • Unlimited data limited to the highest tier subscription plan
  • Limited online resources available for self-help
  • More chart types would be welcomed

Azure Databricks

Azure Databricks

Best for High-Performance Analytics Platform for Azure


Price: Starts from $23,500 per year


Azure Databricks is a data analytics tool specifically optimized for Microsoft’s Azure cloud services. It runs on Apache Spark and integrates well with various open-source libraries. The platform offers three development environments tailored for data-intensive applications: Databricks SQL, Databricks Machine Learning, and Databricks Data Science & Engineering. Users can utilize languages like Python, Java, R, Scala, and SQL, along with popular data science frameworks and libraries such as TensorFlow, scikit-learn, and PyTorch. Pricing options include pay-as-you-go based on compute usage or prepaid packages starting from $23,500 per year.



  • Highly versatile and powerful
  • Supports multiple languages and data science frameworks
  • Integration with Azure cloud services


  • Expensive for smaller teams or projects
  • Dashboards and visualizations could be improved

SAS Visual Analytics

SAS Visual Analytics

Best for Big data analytics tool with smart visualizations


Price: Starts from $8000 per year, with a 14-day free trial available


SAS Visual Analytics allows users to easily import data from various sources including databases, Hadoop, Excel spreadsheets, and social media. It offers a wide range of interactive visualizations such as bar and pie charts, heat maps, animated bubble charts, vector maps, numeric series, tree maps, network diagrams, correlation matrix, forecasting, decision trees, and more. The platform also provides ease-of-use features like one-click filtering and automated content linking.



  • Flexible drag-and-drop analytics elements
  • Supports high volume of simultaneous users without lagging
  • Handles tens of millions of records efficiently
  • BI dashboards accessible across multiple devices


  • Limited number of connection options with third-party apps
  • Could improve HTML5 support
  • Price may be prohibitive compared to other options on this list

Arcadia Enterprise


Best for Telecommunications analytics solution


Price: Customized pricing available upon request


Arcadia Enterprise offers AI-driven data lake analytics and BI software designed to run natively within modern data platforms. It achieved first place in the 2018 Big Data Analytics Market Study by Dresner Advisory Service among 17 other BI vendors. The platform’s in-data-lake BI architecture includes a drag-and-drop web-based interface, an in-cluster analytics engine that scales linearly for ease of management, and embedded analytics for Hadoop and Cloud. Telecom companies can benefit from features such as behavioral churn analysis, service cost controls, and infrastructure impact reports.



  • Freemium version (Arcadia Instant) available for testing
  • Smooth and intuitive interface for data connections and dashboards
  • Handy scheduled mail reporting features


  • Poor integration with Hortonworks Data Platform
  • No mobile app currently available
  • Steep learning curve for IoT analytics and ingest functionality

DNIF Security Information & Event Management (SIEM)

DNIF Security Information & Event Management (SIEM)

Best for Event log management


Price: Starts from $10,586 per month


DNIF offers a highly customizable Security Information & Event Management (SIEM) platform, providing easy visualization of threat detection processes. DNIF HyperCloud is a cloud-native threat detection platform equipped with SIEM, User and Entity Behavior Analytics (UEBA), and Security Orchestration, Automation, and Response (SOAR) capabilities, ensuring unlimited scalability. This low-infrastructure tool rapidly analyzes vast quantities of unstructured log data to identify complex threats. DNIF allows users to build and customize dynamic dashboards with ready-to-go widgets for threat detection, authentication, cloud monitoring, and compliance. It seamlessly integrates with a wide range of operating systems, applications, and security devices.



  • Easy deployment and troubleshooting
  • Quick log search returns
  • Highly scalable platform
  • Easy customization
  • Free trial available


  • Could offer more flexible security use cases

Other Big Data Analytics Tools

Here are some additional big data analytics tools list that didn’t make it to the top list:


11. IBM Cloud Pak for DataL: Best for reducing ETL requests

12. Azure Data Lake Analytics: Best pay-per-job big data solution

13. Semrush: Best big data analytics for ease of use and accessibility

14. Sisense: Best API-first cloud technology

15. Cloudera: Best industrialized enterprise AI

16. Talend: Best data integration with governance

17. iCEDQ: Best for dataops testing and monitoring

18. Bizintel360: Best for analytics without programming knowledge

19. Hortonworks: Best open-source framework for distributed storage

20. Jethro: Best for 1000+ concurrent users

21. CloudMoyo: Best for CIOs and CTOs

22. Altamira Lumify: Best for link analysis

23. Plotly: Best to productionize Python analytics

24. Azure Databricks: Best for Microsoft Suite users

25. Exasol: Best for retail data analytics

26. Omniscope EVO: Best for Chrome browser users

27. Apache Spark: Best open-source big data analytics tool (with Apache Hadoop)

28. Qubole: Best for openness and data workload flexibility

29. Deep.BI: Best for e-commerce and banking

30. Accelerite ShareInsights: Best collaborative rapid insight prototyping

31. MATLAB: Best iterative analysis and design processes


These tools cater to various specific needs and preferences, offering a wide range of functionalities for big data analytics tasks.

How Is Big Data Analyzed?

Big data analysis involves several steps to make sense of large and complex datasets:


  • Data Collection: Gather structured, semi-structured, and unstructured data from various sources such as data lakes, databases, social media, sensors, and more.


  • Data Processing: Cleanse and preprocess the data to ensure accuracy and consistency. This may involve data quality automation tools to identify and correct errors or inconsistencies.


  • Data Analysis: Utilize statistical methods and machine learning algorithms to analyze the data. This includes identifying patterns, trends, correlations, and anomalies within the dataset.


  • Advanced Techniques: Apply advanced techniques such as text analytics, natural language processing (NLP), predictive analytics, and user behavior analytics to extract deeper insights from the data.


  • Predictive Modeling: Develop predictive models to forecast future trends or outcomes based on historical data patterns.


  • Visualization: Present the findings in a visual format, such as charts, graphs, and dashboards, to make the insights easily understandable and actionable for business users.

Importance of Big Data Analytical Tools

Big data analytical tools are essential for businesses and organizations to harness the power of data effectively. Here’s why they are important:


  • Data Processing and Analysis: Big data tools enable processing and analysis of large volumes of data quickly and efficiently. They help uncover valuable insights and patterns that can drive informed decision-making.


  • Decision-Making Support: These tools provide data-driven insights that support strategic decision-making. By analyzing complex datasets, businesses can identify trends, risks, and opportunities to stay competitive.


  • Enhanced Efficiency: With big data tools, organizations can automate data processing tasks, reducing manual effort and improving operational efficiency. This allows teams to focus on higher-value activities.


  • Predictive Analytics: Many big data tools offer predictive analytics capabilities, allowing businesses to forecast trends and outcomes based on historical data. This helps in proactive planning and risk management.


  • Customer Understanding: Analyzing customer behavior and preferences using big data tools helps businesses tailor products, services, and marketing strategies to meet specific needs, enhancing customer satisfaction and loyalty.


  • Real-time Insights: Some tools provide real-time analytics, enabling immediate responses to changing market conditions or operational issues. This agility is crucial for maintaining competitiveness in dynamic industries.


  • Innovation and Growth: Leveraging big data insights fosters innovation by identifying new opportunities and optimizing existing processes. This drives business growth and ensures long-term sustainability.

Types of Big Data Analytics

Big data analytics encompasses various approaches and techniques to extract insights from large and complex datasets. Here are the primary types of big data analytics:

Descriptive Analytics

Descriptive analytics focuses on summarizing historical data to understand what has happened in the past. It involves aggregation, data mining, and visualization techniques to provide a comprehensive view of past performance and trends.

Diagnostic Analytics

Diagnostic analytics aims to determine why certain events occurred by identifying patterns and correlations in data. It involves deeper analysis to uncover root causes of specific outcomes or behaviors.

Predictive Analytics

Predictive analytics uses statistical algorithms and machine learning techniques to forecast future trends and outcomes based on historical data. It helps in anticipating potential scenarios and making proactive decisions.

Prescriptive Analytics

Prescriptive analytics goes beyond predicting outcomes by recommending actions to optimize results. It combines predictive models with optimization algorithms to provide decision-makers with actionable insights.

Real-time Analytics

Real-time analytics involves processing and analyzing data immediately as it is generated, allowing organizations to respond quickly to changing conditions or events. It is crucial for applications requiring instant insights and decision-making.

Text Analytics

Text analytics focuses on analyzing unstructured textual data, such as customer reviews, social media posts, and emails. Natural language processing (NLP) techniques are used to extract meaning, sentiment, and insights from text data.

Spatial Analytics

Spatial analytics involves analyzing geographic or location-based data to uncover insights related to physical spaces. It is used in applications such as urban planning, logistics, and location-based marketing.


Each type of big data analytics serves different purposes and addresses specific business needs. By leveraging these analytics techniques, organizations can gain valuable insights, optimize operations, mitigate risks, and drive innovation in today’s data-driven world.

Big Data Analytics Applications

Big Data Analytics Applications

Big data analytics applications are transforming industries by tapping into vast datasets to reveal powerful insights. Businesses use these insights to make smarter decisions, streamline operations, and deliver better customer experiences.


Big data analytics finds extensive applications across industries:


Healthcare: Analyzing patient data for personalized treatment plans, disease prediction, and healthcare resource optimization.


Retail: Utilizing customer purchase history for targeted marketing, inventory management, and demand forecasting.


Finance: Detecting fraud through transaction monitoring, risk assessment, and algorithmic trading based on market analysis.


Manufacturing: Implementing predictive maintenance, quality control, and supply chain optimization based on sensor data and production analytics.


These applications highlight how big data analytics drives efficiency, innovation, and strategic decision-making in diverse sectors.

Big Data Analytics Tools Comparison Criteria

Here’s a breakdown of the criteria used for comparing big data analytics tools:

User Interface (UI)

  • Does the software effectively present large and complex datasets from various sources in a user-friendly manner?
  • Is the interface intuitive and efficient, allowing users to navigate through the extensive data technologies seamlessly?


  • Does the big data software provide use case-specific tutorials, training resources, and tech support to assist users?
  • Can motivated data science experts manage the full functionality of the tool effectively?


  • Does the big data analytics tool offer easy integration with a variety of common and uncommon data stores such as Hive, Oracle, Azure, Google Cloud, and social media platforms?
  • Are there essential connectors available, such as those for Amazon Web Services (AWS), to facilitate seamless data connectivity?

Value for Money

  • Is the pricing of the big data processing solution scalable based on factors such as data volume, number of data warehouses, and artificial intelligence capabilities?
  • Are all costs transparent, fair, and flexible, ensuring that users receive value commensurate with their investment?


These criteria help assess the effectiveness, usability, compatibility, and cost-effectiveness of big data analytics tools, enabling organizations to make informed decisions based on their specific needs and preferences.

Big Data Analytics Tools Key Features

I have experience with a variety of big data analytics tools, including some of the ones mentioned in your list. Here are my thoughts:


  • Programming Models: It’s essential for big data analytics tools to support various programming models like MapReduce, Message Passing, Directed Acyclic Graph, Workflow, SQL-like, and Bulk Synchronous Parallel to accommodate different data processing needs efficiently.
  • Statistical Algorithms and What-If Analysis: The inclusion of statistical algorithms and what-if analysis capabilities enhances the predictive and exploratory analysis capabilities of big data tools, allowing users to derive deeper insights from their data.
  • Flexible Programming Language Support: The ability to work with different programming languages such as SQL, NoSQL, Java, and Python ensures versatility and compatibility with diverse data processing tasks and preferences.
  • Streamlined API Software: A streamlined, interactive Application Programming Interface (API) software facilitates seamless integration and interaction with other systems and applications, enhancing overall usability and functionality.


Regarding the list of business intelligence and big data analysis tools, it covers a diverse range of functionalities and features essential for effective data management and analysis. However, the choice of tools depends on specific business requirements, data complexity, and organizational preferences.


For day-to-day business analytics, I often use a combination of tools such as Tableau, Python with libraries like Pandas and NumPy for data manipulation and analysis, and SQL for querying databases. These tools provide a comprehensive solution for data exploration, visualization, and analysis.


If I were to add a big data platform to the list, I would consider Apache Kafka for its high-throughput, distributed messaging system, which is widely used for building real-time data pipelines and streaming applications.


In terms of big data visualization tools, my “must-haves” include Tableau for its interactive and intuitive visualizations, and Apache Superset for its open-source, cloud-native analytics platform that offers a wide range of visualization options and integration capabilities.


Overall, the choice of tools depends on specific business needs, technical requirements, and the scalability of the solution to handle large volumes of data effectively.

Frequently Asked Questions

Big data analytical tools are software solutions designed to process, analyze, and derive insights from large and complex datasets. These tools utilize advanced algorithms, machine learning techniques, and statistical models to uncover patterns, trends, and correlations within the data.

Big data analytical tools work by ingesting, processing, and analyzing vast amounts of structured, semi-structured, and unstructured data from various sources. They employ algorithms and models to identify meaningful patterns and relationships, which are then visualized through interactive dashboards or reports for further analysis and decision-making.

Big data analytical tools typically offer a range of features, including data visualization, predictive analytics, machine learning capabilities, data integration, and support for various programming languages. They may also provide advanced statistical algorithms, what-if analysis, and flexible APIs for seamless integration with other systems.

When selecting a big data analytical tool, consider factors such as the tool’s user interface, usability, integrations with other data sources, pricing, and scalability. Assess your specific business requirements, data complexity, and analytical needs to determine which tool best aligns with your objectives.

Some popular big data analytical tools include Tableau, Splunk, Qlik Sense, SAS Visual Analytics, Apache Spark, Hadoop, and Azure Databricks. These tools offer a range of functionalities for data processing, analysis, and visualization to meet diverse business needs.

Big data analytical tools can provide several benefits to businesses, including improved decision-making, enhanced operational efficiency, better understanding of customer behavior, identification of new opportunities, and competitive advantage. By leveraging data-driven insights, organizations can optimize processes, innovate more effectively, and drive growth.