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Big data analytics tools provide a wide range of capabilities to help enterprise organizations derive valuable insights from the vast amounts of data they collect. Because they’re designed to work with huge volumes of structured and unstructured data on a large scale, big data analytics tools generally have more specific features than other data analytics platforms.
To help you find the right tool for your business, we’ve reviewed the best big data analytics tools on the market—here are our picks for the top big data analytics tools for teams and businesses in 2024:
- Zoho Analytics: Best software for self-service BI
- Tableau: Best analytics tool for data visualization
- Microsoft Power BI: Best tool for big data preparation
- Oracle Analytics Cloud: Best for analytics automation
- SAS Visual Analytics: Best for visual data exploration
- Sisense: Best software for embedded analytics feature
- TIBCO Spotfire: Best for advanced analytics capabilities
- Splunk: Best data analytics tool for Hadoop integration
- Looker: Best analytics software for data storytelling
- Apache Spark: Best analytics tool for big data workloads
Featured Partners: Business Analytics Software
Best Big Data Analytics Tool Comparison
While the tools and software included in this list provide robust big data analytics features, each option has distinct offerings, as well as their pros and cons. Here’s an at-a-glance comparison of how each option stacks up in five major categories. Note that all per-user prices are with a one-year commitment, unless otherwise noted.
| Core Features | Advanced Features | Ease of Use | Customer Support | Pricing (Annual) |
|
|---|---|---|---|---|---|
| Zoho Analytics | Data integration, collaborative tools, security and governance | Business intelligence, AI assistant, data visualization | 3.6/5 | 3.4/5 | Free to $455 per user, per month for 50 users |
| Tableau | Data import and export, embedding playground, decision trees | Custom data labels, Tableau GPT, predictive modeling | 3.6/5 | 3.5/5 | $15 to $75 per user, per month |
| Microsoft Power BI | Self-service data preparation, products documentation, report themes | Advanced AI, dataflows, query catching | 3.6/5 | 3.2/5 | Free to $20 per user, per month |
| Oracle Analytics Cloud | Data import and export, mobile analytics app, data collaboration | Enterprise data modeling, Machine learning, developer tools | 3.1/5 | 3.4/5 | $16 to $80 per user, per month |
| SAS Visual Analytics | Text and visual analytics, forecasting, interactive dashboards | Augmented analytics, cloud native computing, chat-enabled insights | 3.6/5 | 4.3/5 | Contact sales for quote |
| Sisense | Data modeling and blending, visual dashboards, embed code | Generative AI, ad hoc analysis, data discovery | 3.9/5 | 3.7/5 | Contact sales for quote |
| TIBCO Spotfire | Visual analytics, Data preparation | Advanced analytics | 3/5 | 2.8/5 | Contact sales for quote |
| Splunk | Data Import and Export, Neural network | Artificial intelligence | 3.6/5 | 3.1/5 | Contact sales for quote |
| Looker | Market Basket Analysis, Time series | Machine learning, Business Intelligence | 3.4/5 | 2.5/5 | $5,000 per month (billed monthly) |
| Apache Spark | Real-time data stream processing, Lightning fast processing | Artificial intelligence, Advanced analytics | 3.6/5 | 1.3/5 | Free to use |
Table of Contents
Zoho Analytics
Best for self-service BI
Overall rating: 4.3/5
- Core Features: 4.5/5
- Advanced Features: 4.1/5
- Customization & Integration: 4.6/5
- Cost: 4.8/5
- Ease of Use: 3.6/5
- Customer Support: 3.4/5
Zoho Analytics is a self-service BI platform, which means you can easily extract insights from your data without the assistance of IT staff or professional data scientists. Users can gather and manage big data from multiple sources, including files stored locally, on cloud drives, web feed URLs, and even their companies’ custom-built applications. A series of permission levels let users ranging from C-suite pros to sales reps in the field access the data they need.
Pros and Cons
| Pros | Cons |
|---|---|
| Offers robust third-party app integrations | Advanced tools can be challenging to use |
| Provides transparent pricing and terms | Users report issues with data synchronization |
| Facilitates collaboration between teams | Declining performance with large datasets |
Pricing
- Free to $30 per user, per month for On-Premise pricing
- $24 per month (2 users) to $455 per month (50 users) for Cloud pricing
- Contact sales for quote for Custom pricing
- 15-to-30 day free trial available
Features
- Zoho’s Zia AI assistant lets you ask questions and get relevant reports for answers.
- Users can gather and blend data from multiple sources to build reports and dashboards.
- Teams can collaborate, share reports, and customize access controls.
- Zoho keeps your data exports protected with passwords and strong auto-encryption.
Tableau
Best for data visualization
Overall rating: 4.2/5
- Core Features: 4.7/5
- Advanced Features: 4.1/5
- Customization & Integration: 4.8/5
- Cost: 3.6/5
- Ease of Use: 3.6/5
- Customer Support: 3.5/5
Tableau is a leading visual analytics platform with VizQL (Visual Query Language) technology, which powers visualization and interactive data exploration. Users can create data visualizations without the need to first organize data, which allows them to navigate through query languages even without prior knowledge. Tableau’s VizQL technology also works as a translator of structured query language (SQL) queries to easily turn tabular data into visual analysis.
Pros and Cons
| Pros | Cons |
|---|---|
| Allows users to create interactive visualizations | Platform can be resource intensive |
| Connects with a wide range of data sources | Reports need to be refreshed manually |
| Fast data lookup and analysis | Data processing feature can be lacking |
Pricing
- $15 to $75 per user, per month
- 14-day free trial available
Features
- Tableau allows you to develop custom codes to embed interactive visualizations.
- Users can easily build, summarize, and customize reports via a drag-and-drop interface.
- Tableau GPT helps turn data into actionable insights and generate visualizations.
- Use linear regressions to build predictive models about existing data.
Microsoft Power BI
Best for big data preparation
Overall rating: 4.2/5
- Core Features: 4.7/5
- Advanced Features: 4.1/5
- Customization & Integration: 4.8/5
- Cost: 3.6/5
- Ease of Use: 3.6/5
- Customer Support: 3.5/5
Microsoft Power BI is a big data analytics tool with leading data preparation capabilities, including a self-service feature, AI-driven analytics, and a Common Data Model (CDM). Users can easily ingest, cleanse, and schematize data from a variety of sources and create workflows with familiar tools such as Power Query. Data engineers can also leverage Azure Data Services, while analysts can take advantage of CDM to ensure consistency in their business processes.
Pros and Cons
| Pros | Cons |
|---|---|
| Cost-effective and transparent pricing plans | Interface can be complex for beginners |
| Provides extensive documentation | Platform has limited online access |
| Best suited for Microsoft ecosystem |
Pricing
- Free to $20 per user, per month
- 60-day free trial available
Features
- Free Microsoft Fabric Preview account lets you create robust, interactive reports via Power BI.
- Create datasets from a wide array of sources and add them to a centralized data hub.
- Pull out reports and summaries instantly, as well as generate data analysis expressions (DAX) calculations with Power BI’s Copilot feature.
- Unify enterprise-scale and self-service BI to share at every level in your organization.
Oracle Analytics Cloud
Best for automating analytics processes
Overall rating: 4.2/5
- Core Features: 4.5/5
- Advanced Features: 4.7/5
- Customization & Integration: 4.8/5
- Cost: 3.5/5
- Ease of Use: 3.1/5
- Customer Support: 3.4/5
Oracle Analytics Cloud is a big data analytics platform with automation capabilities for different types of analytics and big data analysis use cases. Organizations can set up triggers to send out analyses, dashboards, and other resources to targeted audiences on-demand or on a schedule. Organizations that are already used to using Oracle tools, including Oracle’s namesake database, will likely be the most attracted to the Analytics Cloud offering.
Pros and Cons
| Pros | Cons |
|---|---|
| Integrates data from a variety of sources | Dependent on data warehouse quality |
| Powerful data visualization | Limited offline access |
| Offers self-service data preparation |
Pricing
- Free to $20 per user, per month
- 60-day free trial available
Features
- The platform’s smart insight features help you automate generating and sharing data.
- Monitor business performance from anywhere with its mobile analytics application.
- Use machine learning features even without special technical and programming skills.
- Protect your data with role-based and data-level security and access control.
SAS Visual Analytics
Best for visual data exploration
Overall rating: 3.9/5
- Core Features: 4.6/5
- Advanced Features: 4.8/5
- Customization & Integration: 4.5/5
- Cost: 1.1/5
- Ease of Use: 3.6/5
- Customer Support: 4.3/5
SAS Visual Analytics is a low-code analytics platform built for users and organizations looking for deep analytics tools. Create advanced visualizations with its drag-and-drop functionality and quickly identify relationships in data using suggestions and related measures. Leverage machine learning and natural language features for spotting, visualizing, and narrating insights, and automatically highlight key relationships, outliers, and clusters for in-depth analysis.
Pros and Cons
| Pros | Cons |
|---|---|
| Robust data visualization options | Dependent on SAS ecosystem |
| Suited for collaboration | Plans can be costly |
| Self-service analytics features |
Pricing
- Contact sales for quote
Features
- Users can automatically create and visualize data charts to better understand what the data means.
- Colleagues can collaborate via shared dashboards, analysis, and reporting across multiple platforms, including web, mobile, and Microsoft Office integration.
- The platform easily enables authors to build interactive and custom reports.
- Users can leverage its predictive analytics features to assess possible outcomes and make smarter, data-driven decisions for their businesses.
Sisense
Best for embedded analytics
Overall rating: 3.8/5
- Core Features: 4.3/5
- Advanced Features: 4.7/5
- Customization & Integration: 4.6/5
- Cost: 1/5
- Ease of Use: 3.9/5
- Customer Support: 3.7/5
Sisense is a cloud-native BI platform offering an intuitive embedded analytics feature. Users can choose the method that works best for them, ranging from the pro-code Compose SDK to the no-code or low-code iFrame. Sisense is a good choice for larger organizations looking for fast implementation time. Its data visualization via the systems dashboard is often seen by users as being easy to use and as a time saver to get the required results.
Pros and Cons
| Pros | Cons |
|---|---|
| Robust embedded analytics feature | Can be resource intensive |
| High performance even with large datasets | Limited native data connectors |
Pricing
- Contact sales for quote
Features
- Users can track metrics, embed dashboards, and apply filters for a customizable and interactive data visualization.
- Sisense can connect to a wide range of data sources, including databases, spreadsheets, cloud services, and web applications.
- Easily turn data into actionable insights with prebuilt templates and API capabilities.
- Turn copies of your dashboard or meeting minutes into PDF reports for sharing.
TIBCO Spotfire
Best for advanced analytics
Overall rating: 3.8/5
- Core Features: 4.5/5
- Advanced Features: 5/5
- Customization & Integration: 4.4/5
- Cost: 1.4/5
- Ease of Use: 3/5
- Customer Support: 2.8/5
TIBCO Spotfire is an AI-based analytics platform offering powerful self-service analytics and data visualization tools for analyzing and sharing actionable insights. Users can combine data-at-rest with data-in-motion with Spotfire and ingest both structured and unstructured data from distinct sources. It also supports what-if analysis and prescriptive analytics across a wide range of use cases, so users can explore and model for insights most valuable to them.
Pros and Cons
| Pros | Cons |
|---|---|
| Robust embedded analytics feature | Can be resource intensive |
| High performance even with large datasets | Limited native data connectors |
Pricing
- Contact sales for quote
Features
- Users can choose the exploration style that suits their needs via AI-powered recommendations, natural language-driven search, or traditional direct manipulation.
- Spotfire allows users to clean data by reformatting, correcting errors, and combining various datasets.
Splunk
Best for Hadoop integration
Overall rating: 3.7/5
- Core Features: 4.3/5
- Advanced Features: 4.6/5
- Customization & Integration: 4.3/5
- Cost: 1.4/5
- Ease of Use: 3.6/5
- Customer Support: 3.1/5
Splunk is a software platform popular for its web-based log inspection and analytics capabilities, which enable users to look into big data stores in Hadoop systems. The platform benefits from a proven collaboration component and enables users to create and share graphs and analytics dashboards. For those organizations already using Splunk for log or other types of analysis, embracing Splunk Analytics for Hadoop is an easy step.
Pros and Cons
| Pros | Cons |
|---|---|
| Robust embedded analytics feature | Can be resource intensive |
| High performance even with large datasets | Limited native data connectors |
Pricing
- Contact sales for quote
Features
- Splunk connects to all the major Hadoop Big Data distributions.
- Splunk Enterprise’s interface allows users to directly make requests on raw data.
- Only authorized entities can access your organization’s secured data with Splunk’s role-based access control feature.
Looker
Best for data storytelling
Overall rating: 3.5/5
- Core Features: 3.3/5
- Advanced Features: 4.5/5
- Customization & Integration: 4.3/5
- Cost: 2/5
- Ease of Use: 3.4/5
- Customer Support: 2.5/5
Looker is a cloud-based BI platform designed to tell stories through reports and visualizations. Looker’s data model provides users with a consistent and governed real-time view of their data across multiple clouds, making it easier for businesses to build insight-powered workflows. Looker’s business intelligence feature, combined with Looker Studio’s customizable charts and interactive dashboards, helps users create more compelling and proactive insights.
Pros and Cons
| Pros | Cons |
|---|---|
| Robust embedded analytics feature | Can be resource intensive |
| High performance even with large datasets | Limited native data connectors |
Pricing
- Starts at $5,000 per month
Features
- Users can access, analyze, and create smart decisions with Looker’s up-to-date version of business data.
- Simplify the creation of reports and dashboards with its enterprise-level BI features to avoid stale data and siloed approaches for your business.
- Take advantage of prebuilt integrations and tailor your platform to your needs with Looker’s robust APIs.
Apache Spark
Best for big data workloads
Overall rating: 3.5/5
- Core Features: 3.4/5
- Advanced Features: 4.3/5
- Customization & Integration: 2.9/5
- Cost: 4.7/5
- Ease of Use: 3.6/5
- Customer Support: 1.3/5
Apache Spark is an open-source platform which offers a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. Users can perform exploratory data analysis (EDA) on petabyte-scale data (with one petabyte equivalent to 1,024 TB) without having to resort to downsampling. Apache also utilizes memory caching, which keeps the data for users to quickly retrieve the next time it’s needed.
Pros and Cons
| Pros | Cons |
|---|---|
| High processing speed due to in-memory processing and data parallelism capabilities | Requires a lot of memory for processing large datasets |
| Requires a lot of memory for processing large datasets, which can be costly | Customer support could be better |
| Self-service analytics features |
Pricing
- Free to use
Features
- Users can stream processes, which allows them to deal with continuous, real-time data streams.
- Apache helps organizations and businesses dramatically improve productivity and performance by helping them process tasks in memory easily.
6 Key Features of Big Data Analytics Tools
Big data analytics tools provide a wide range of features and capabilities, but the top solutions offer some key functionalities for various big data applications. To determine the best big data analytics tools for your business, consider the features detailed below.
Analytic Capabilities
There are multiple types of analytics capabilities with different models for various types of analysis including: predictive data mining, decision trees, time series, neural networks, path analysis, market basket analysis, and link analysis.
Integration
Organizations often need additional statistical tools and programming languages (such as R) to conduct other forms of custom analysis. In such cases, the ability to integrate with their data analytics platform is essential.
Data Import and Export
Getting data in and out of various tools is a critical feature and understanding how difficult (or easy) it is to connect the analytics tool to the big data repository is a key consideration.
Visualization
Seeing the numbers is one thing, but having data displayed in a graphical format often makes the data more usable.
Scalability
Big Data can be big to start with, and generally has a tendency to grow even bigger over time. Organizations need to consider and understand the scalability options for the analytics tools they choose.
Collaboration
Analysis can sometimes be a solitary exercise, but more often than not it involves collaboration.
How we Evaluated the Big Data Analytics Tools
We reviewed the best big data analytics tools based on six primary weighted criteria consisting of specific subcriteria. We then assigned criteria scores out of five for each tool and totaled the scores, identifying a winner in each category as well as the overall top big data analytics tool. Finally, we assigned a primary use case to each big data analytics tool that made our list.
Evaluation Criteria
We started by assessing the core features and advanced tools offered by each option, since the top options offer standard big data technologies and premium features. We then looked into customization and integration, followed by cost and ease of use. Customer support solutions wrapped up our review.
Core Features | 25 percent
We assessed the key capabilities offered by each option including data import and export, time series tool, visual analytics, and predictive mining. Criteria Winner: Tableau
Advanced Features | 20 percent
We looked into premium features such as data visualization, scalability, AI/machine learning, and more. Criteria Winner: TIBCO Spotfire
Customization & Integrations | 20 percent
Here, we evaluated each tool capability to tailor reports, dashboards, user access, and other features to their organization’s needs. We also looked at how well each option syncs with third-party integrations like Microsoft, Hadoop Technology, and more. Criteria Winner: Tableau
Cost | 15 percent
This includes the pricing for basic and enterprise tiers, free trials, billing options, and pricing transparency. Criteria Winner: Microsoft Power BI
Ease of Use | 10 percent
We considered how real users across certified review sites perceive the overall design of the tool’s interface and learning curve. We also looked at each option’s knowledge base or learning resources available. Criteria Winner: Sisense
Customer Support | 10 percent
We checked user feedback and ratings on reputable sites and looked into the support solutions offered by each tool via live chat, phone, and email. Criteria Winner: SAS Visual Analytics
Frequently Asked Questions (FAQs)
What Are Big Data Analytics Tools and Why Are They Important?
Big data analytics tools are software or platforms designed to help organizations and businesses process, analyze, and visualize large sets of complex data. These tools enable users to generate valuable insights from massive data repositories, which are used to derive actionable insights for improving productivity, driving revenue, and optimizing performance.
What Are Commonly Used Tools for Streaming Big Data Analytics?
The most commonly used tools for streaming big data analytics are usually found in engines that allow data to be published, subscribed to, and processed in real-time. Platforms that stream process large volumes of data include features like windowing, event-time processing, and fault tolerance for accurate data analysis.
How Do I Determine Which Big Data Analytics Tool is the Right Fit for My Business?
To determine which big data analytics tools suit your business, assess your goals, resources, and big data challenges you’d like to address. Also consider key factors like your desired data volume, processing speed, and scalability when narrowing down analytics tools to evaluate. Dig deeper into each option’s features and limitations, as well as seek professional advice or learn best practices in your industry, to find a tool that aligns with your needs.
Bottom Line: Best Big Data Analytics Tool
Big data analytics tools enable businesses and organizations to examine large volumes of data and extract actionable insights that drive growth, productivity, and revenue. While most analytics tools offer standard features for data processing, reporting, and visualization, each option differs in its advanced features, customization, and usability. Let our list be a guide in determining which big data analytics tools have the overall capabilities that better suit your business.
If you’d like to browse through other data analytics platforms, see our in-depth review of the top data analytics tools and software.
