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With so many different big data analytics tools available, figuring out which is right for you is tough. You know you want to efficiently leverage complex data to inform strategic decisions but need to figure out which tool is best. I've got you! In this post I'll help make your choice easy, sharing my personal experiences using dozens of different big data analytics software with various large datasets, with my picks of the best big data analytics tools.

What Are Big Data Analytics Tools?

Big data analytics tools are software that process, analyze, and extract meaningful insights from large and complex sets of data. These tools handle vast amounts of structured and unstructured data, utilizing advanced techniques like machine learning, predictive analytics, and data mining to reveal patterns, trends, and relationships.

The benefits and uses of big data analytics tools include enabling data-driven decision-making, enhancing business intelligence, and providing deep insights into customer behavior, market trends, and operational efficiencies. They empower organizations to anticipate future trends, identify new opportunities, and optimize processes. By leveraging big data analytics, businesses can gain a competitive advantage, innovate more effectively, manage risks better, and ultimately drive growth and success.

Overviews Of The 10 Best Big Data Analytics Tools

Here’s a brief description of each big data analytics platform on my list, showcasing what it does best, plus screenshots to showcase some of the features.

Best free data analytics tool

  • 15-day free trial + free demo
  • From $24/month
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Rating: 4.2/5

Zoho Analytics is a self-service BI and analytics software used by the likes of Hyundai, Ikea, HP, and Philips. Their freemium plan is a bit feature lite but you can add up to 2 users, input up to 10K rows/records, and access unlimited reports and dashboards. This is a pretty sturdy offering for free-to-use data analysis. Zoho Analytics comes with a library of pre-built visualizations divided by function (social media, finance, IT, sales) to help you get started.

Zoho Analytics costs from $24/month for 2 users and offers a free 15-day trial. They also have a free plan for 10K rows/records or less.

Pros and cons


  • Building or customizing reports and dashboards is super easy
  • Excellent embedded AI feature (called ZIA)
  • Generate reports right from SQL queries
  • Feature expansion through connection with Zoho’s other apps


  • Hourly data sync not included in entry level plan
  • Cannot auto export data straight to Google Drive
  • Dashboards seem a bit cramped and busy

Best big data analytics tool for ease of use

  • 14-day free trial
  • From $70/user/month
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Rating: 4.4/5

Tableau is a user-friendly, intuitive visual analytics platform with built-in best practices for data exploration and informational storytelling. Users can access their full suite of self-service prep and analytics tools with a minimal learning curve, leveraging drag-and-drop visualizations and easy point-and-click AI-driven statistical modeling. Most users should be able to assemble data to their liking without advanced programming or special commands.

Tableau costs from $70/user/month and offers a free 14-day trial.

Pros and cons


  • Good native integration with Salesforce CRM
  • Comes with robust mobile app for iOS and Android
  • Offers a hearty variety of chart types (Sankey, Doughnut, Maps)
  • Easy to use with self-learning module available


  • Some data manipulation required in order to successfully match queries
  • Limited room for columns when assembling worksheets
  • Frequently requires saved database connections to be re-authenticated

Best for user behavior analytics

  • offers a free plan that allows you to index only 500 MB/day.
  • $2000/year for 1 GB/day
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Rating: 4.2/5

Splunk is currently used by 91 of the Fortune 100 companies, including Intel, Comcast, and Coca-Cola. Splunk offers machine learning-centric visibility and detection of entity profiling and scoring, risk behavior detection, anomaly observation, and high fidelity behavior-based alerts. You can access a free cloud-based sandbox trial of Splunk UBA to check it out before committing. They offer dedicated solutions to DevOps, Security, IT, and big data.

Splunk costs from $2000/year for 1 GB/day and offers a free plan that allows you to index only 500 MB/day.

Pros and cons


  • Flexible data and report sharing using URL links
  • Quick log queries across different types of infrastructure
  • Search queries can be saved for repeat use or converted into apps
  • Can set up detailed, specific alerts for various KPIs


  • Infrastructure maintenance requires more manpower than some competitors
  • Query builder may be prohibitive for non-technical users
  • Steep learning curve compared to others

Best agile data warehousing

  • Free Demo
  • $20/workspace/month
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Rating: 4/5

GoodData is a big data analytics platform that provides users the tools, runtimes, and storage for data ingestion, preparation, transformation, and analytic queries. They boast 50+ connectors for data ingestion/synchronization and offer an Agile data warehousing system on higher tier plans. Their per-workspace pricing model lets unlimited users access sets of data models, metrics, calculations, and dashboards according to a flexible permissions system.

GoodData costs from $20/workspace/month and offers a free demo. They also have a free plan that includes 5 workspaces and up to 100 MB/workspace.

Pros and cons


  • Non-technical users can build dashboards and views easily
  • Excellent integration with Salesforce, Pardot, Zendesk
  • Good for scheduling reports according to exact times and frequencies
  • Provides easy linking of disparate data sources for comparison


  • Coding knowledge required for inquiries and report building
  • Datasets of 100M+ rows may stall performance
  • Some data model adjustments might require customer support

Best big data analytics tool with smart visualizations

  • 14 Days Free Trial
  • $8000/year

With SAS Visual Analytics, users are able to easily import data from databases, Hadoop, Excel spreadsheets, and social media. They offer a huge variety of interactive visualizations, including bar and pie charts, heat maps, animated bubble charts, vector maps, numeric series, tree maps, network diagrams, correlation matrix, forecasting, decision trees, and more. Plus they have ease-of-use options like one-click filtering and automated content linking.

SAS Visual Analytics costs from $8000/year and offers a free 14-day trial.

Pros and cons


  • Flexible drag-and-drop analytics elements
  • Well-suited to support high volume of simultaneous users
  • Works with tens of millions of records without lagging
  • Quality BI dashboards can be accessed across many devices


  • Low number of connection options with third-party apps
  • Could use better HTML5 support
  • May be price prohibitive compared to others on this list

DNIF HyperCloud is a cloud-native threat detection platform with SIEM, UEBA, and SOAR capabilities and unlimited scalability. This low-infrastructure tool can rapidly analyze vast quantities of unstructured log data and spot patterns to identify complex threats. DNIF allows you to build and customize dynamic dashboards and comes with ready-to-go widgets for threat detection, authentication, cloud monitoring and compliance.DNIF seamlessly integrates with a wide range of operating systems, applications, and security devices. DNIF pricing starts at $10,586 per month on an annual commitment.Pros:Easy to deploy and troubleshoot,Quick log search returns,Highly scalable,Easy to customize,Free trial, Cons:Could offer more flexible security use cases

Best pay-per-job big data solution

  • $1 per 1,000 runs or $0.25/DIU-hour

Azure Data Lake Analytics is an on-demand analytics job service that prices per-job, ensuring that you only pay for the processing as you use it. This tool can process petabytes of data for business intelligence (BI) as well as sentiment analysis. You’re left with high-impact visualizations of your relational source data, such as Azure SQL Database and Azure Synapse Analytics.

Azure Data Lake Analytics costs from $1 per 1,000 runs or $0.25/DIU-hour and scales according to your use case.

Pros and cons


  • Simple solution for batch workloads
  • Complimentary storage of relational database and NoSQL
  • Works well with power BI services for reporting
  • Only pay for consumed ADLUs


  • Lack of streaming option and event processing
  • May be confusing for users coming from a primarily MSBI background
  • Lacks resources for end-user training

Best for reducing ETL requests

  • 7 Days Free Trial
  • $800/month

IBM Cloud Pak for Data is a fully-integrated, cloud native, data and AI platform designed for sophisticated DataOps and business analytics solutions. IBM boasts a potential for a 25-65% reduction in extract, transform, load (ETL) requests by eliminating the complexities of data integration of different data types and structures using Cloud Pak for Data. You can customize your workflow using their flexible API and complimentary proprietary and third-party services.

IBM Cloud Pak for Data costs from $800/month and offers a 7-day free trial.

Pros and cons


  • Award-winning data security solutions
  • Good for optimizing storage and other maintenance of preexisting data
  • Tailored solutions for lessening your ETL request load


  • Could use more options to better migrate data from other cloud providers to IBM
  • May be cost prohibitive for smaller enterprises
  • Their Db2 database has a bit of a clunky, old-fashioned feel

Best big data analytics tool for purchasing departments

  • 30 Days Free Trial
  • $30/user/month

Qlik Sense is an end-to-end data analytics platform with a unique associative analytics engine that lets users search and explore across all data in any direction with no pre-aggregated data or predefined queries to limit you. Purchasing departments will get the most use out of in-depth supplier and industry trend comparisons, easy currency filters for international partners, and low product or low spend reports.

Qlik Sense costs from $30/user/month and offers a free 30-day trial.

Pros and cons


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


  • Not as customizable as others, like Tableau
  • Minimal non-interactive report creation options
  • Low res monitors may struggle to clearly display

Best big data analysis for start-ups

  • Free Trial
  • $2500/year

Qrvey is an embedded analytics platform used for SaaS data, analytics, and automation technologies. You can deploy it right into your pre-existing AWS account in order to visualize your entire data pipeline. Their start-ups package includes specialized support for pre-launch or early-launch companies, like quick installation and launch, serverless analytics scalability, no-code embedded widgets, up to 10 GB data, and a lower entry subscription price point.

Qrvey costs from $2500/year and offers both a free demo and a free trial.

Pros and cons


  • Good out-of-the-box workflows/automation tool
  • Ability to embed a chart into your own web app without iFrames
  • Unlimited users and API calls for ever plan tier


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

The Best Big Data Analytics Tools Summary

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Compare Software Specs Side by Side

Use our comparison chart to review and evaluate software specs side-by-side.

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Other Big Data Analytics Tools

Here’s a few more that didn’t make the top list.

  1. Arcadia Enterprise

    Best telecommunications analytics solution

  2. Azure Databricks

    Best High-Performance Analytics Platform for Azure

  3. Semrush

    Best big data analytics for ease of use + accessibility

  4. Sisense

    Best API-first cloud technology

  5. Talend

    Best data integration with governance

  6. Cloudera

    Best industrialized enterprise AI

  7. Bizintel360

    Best for analytics without programming knowledge

  8. iCEDQ

    Best for dataops testing and monitoring

  9. Hortonworks

    Best open source framework for distributed storage

  10. MATLAB

    Best iterative analysis and design processes

  11. Qubole

    Best for openness and data workload flexibility

  12. Apache Spark

    Best open-source big data analytics tool (with Apache Hadoop)

  13. Omniscope EVO

    Best for Chrome browser users

  14. Exasol

    Best for retail data analytics

  15. Azure Databricks

    Best for Microsoft Suite users

  16. Plotly

    Best to productionize Python analytics

  17. Accelerite ShareInsights

    Best collaborative rapid insight prototyping

  18. DNIF Big Data Analytics

    Best event log management

  19. Jethro

    Best for 1000+ concurrent users

  20. CloudMoyo

    Best for CIOs and CTOs

  21. Altamira Lumify

    Best for link analysis

  22. Deep.BI

    Best for e-commerce and banking

How Is Big Data Analyzed?

To put it simply: Big data is analyzed by collecting structured semi-structured and unstructured data from your data lakes and parsing out what's most relevant to your current informational need most likely using some form of data quality automation to do so.

Then, you leverage statistics and machine learning to parse through the data ecosystem and compile predictive analytics, user behavior analytics, and other metrics. This process might also include text analytics, natural language processing, predictive analytics, and so forth.

All of this works to create end reports that are readable and actionable for business users.

Big Data Analytics Tools Comparison Criteria

Here’s a summary of my evaluation criteria: 

  1. User Interface (UI): Does the software convey large, complex data sets stemming from myriad sources in an easy to understand, intuitive, and efficient way? Can users reasonably find their way around the large-scope data technologies?
  2. Usability: Big data analysis comes in many shapes and does many things—does the big data software offer use case-specific tutorials, training resources, and tech support? Is the full functionality of the tool manageable for motivated data science experts?
  3. Integrations: Big data analytics tools must connect to an assortment of common and uncommon data stores—Hive, Oracle, Azure, Google Cloud, and social media. There are some must-haves; for example, easy connectors with Amazon Web Services (AWS).
  4. Value for $: Pricing of big data processing solutions must be scalable according to the amount of data, number of data warehouses, artificial intelligence capabilities, and other metrics. Are all costs fair, transparent, and flexible?

Big Data Analytics Tools Key Features

  1. Inclusive of a variety of programming models, like MapReduce, Message Passing, Directed Acyclic Graph, Workflow, SQL-like, and Bulk Synchronous Parallel
  2. Statistical algorithms and what-if analysis
  3. Flexible programming language accommodations (ex. SQL and NoSQL, Java, Python)
  4. A streamlined, interactive application programming interface software (APIS)

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Which Big Data Analytics Tools Have You Used?

What do you think about this list of business intelligence and big data analysis tools? What data management tools do you use for your business analytics on a day to day basis? Do you have a big data platform in mind that you would add to this list if you could? What big data visualization tools are your "must-haves" on-premise or in the cloud? Let us know in the comments section.

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By Jason Boog

Over his 15-year career, Jason Boog has worked as a QA tester, QA analyst, and Senior QA Analyst on video games, commercial sites, and interactive web applications. He spent more than a decade building out the QA team and process as Director of Quality & Client Support at a full-service digital agency.