Categories   >   Big Data Visualization Tools
Last Reviewed: November 13th, 2024

Best Big Data Visualization Tools

What are Big Data Visualization Tools?

Big data visualization tools are like high-powered translators, taking mountains of complex data and turning them into easy-to-understand charts, graphs, and dashboards. Imagine a weather map swirling with information – these tools untangle it, revealing weather patterns at a glance. This makes big data accessible, solving the problem of information overload and allowing businesses to glean valuable insights. Their importance is undeniable. By visualizing sales trends or customer behavior, companies can make data-driven decisions, improving marketing campaigns or product development. These tools aren't just static pictures; they're interactive, allowing users to drill down and explore specific data points. Think of it as zooming in on a specific region on the weather map to see the temperature of a particular city. These benefits extend far beyond the business world. Researchers can use them to visualize complex scientific data, like protein structures, accelerating breakthroughs. While limitations exist, like the need for clean data and skilled users to create effective visualizations, big data visualization tools are a game-changer, unlocking the power of information for a wide range of users.

What Are The Key Benefits of Big Data Visualization Tools?

  • Clear Insights
  • Faster Decisions
  • Identify Trends
  • Improved Communication
  • Data Exploration
  • Spot Anomalies
  • Enhanced Storytelling
  • Increased Engagement
  • Boost Productivity
Read more

Overall

Based on the latest available data collected by SelectHub for 52 solutions, we determined the following solutions are the best Big Data Visualization Tools overall:

Hadoop
Hadoop
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Why We Picked Hadoop

Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it.

So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

Pros & Cons

  • Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
  • Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
  • Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
  • Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
  • Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
  • Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
  • Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
  • Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.

Key Features

  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
Tableau Big Data
Tableau Big Data
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Deployment
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Platform
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Key Features

  • Big Data Integrations: Connects seamlessly to all kinds of data infrastructures and sizes of data sets, including Big Data. Its ecosystem integrates with Hadoop distributions, NoSQL databases and Spark sources with ease. 
  • One Data Interface: Consolidate and democratize data in one place. Invite all members of an organization to explore data, asking more questions and finding more answers.
  • VizQL: Interact with data in real time with a proprietary technology that allows for limitless exploration that’s more accessible to all skill levels than traditional coding.
  • Data Catalog: Take advantage of data governance with a glossary of data sources and standard data definitions, as well as metadata management and standardization of data procedures. Understand data in context and easily find the right data for the job within vast data sets.

Pricing

License/Subscription Cost
  • Based on the number of users
  • Based on an annual subscription model – per user, per month (billed annually)
  • Subscription costs allow users to access Tableau over a set timeframe for either an on-premise or cloud-based/SaaS model
  • Subscription licensing model requires a one-time license fee
  • Provides enterprise subscriptions/enterprise licensing based on the type of SMB and the enterprising plan selected with both the on-premise and cloud-based/SaaS model
Maintenance Cost
  • Cost is included in the price of subscription license. This feature includes ongoing updates, access to Tableau’s ongoing product upgrades and support services at no additional charge
Installation/Implementation Cost
  • Included in the upfront subscription cost
  • No hidden costs
Customization Cost
  • The custom version cannot be requested. Instead, users can do limited customizations on their own, such as:
  • User can change the server name that appears in the browser tab, tooltips and messages
  • User can change the logos that appear in different server page contexts
  • User can control the language used for the server user interface and the locale used for views
  • Custom fonts can be installed for different clients
  • Administrators and project leaders can also add images for projects in thumbnail view
Data Migration Cost/Change Management/Upfront Switching Cost
  • Data Migration is possible in Tableau Servers and can be done with the following tools: Tabcmd Script, REST API, TabMigrate and Enterprise Deployment Tool by InterWorks
  • There is a limit to how much data can be stored within Tableau Online and the storage cost will increase if that threshold is crossed
Training Cost
  • The price will vary depending on the type of training opted for. Options include live online training, classroom training, eLearning and certification programs.
  • Tableau provides free training videos, whitepapers and product demos for streamlining the implementation process.

Two types of training can be obtained:

  • In-person training takes two days, and classroom sessions are available in various global locations
  • Virtual classes are held four times a month and have a duration of 4 to 5 days
  • The cost of training is $1,400 per person for each type of training
  •  Web Authoring training costs $700 per person

eLearning is a year-long subscription that gives users access to two courses:

  • Desktop I: Fundamentals and Desktop II: Intermediate
  • The cost of Tableau Certification varies depending on the type of course that a user takes
  • Tableau Desktop 10: Qualified Associate ($250/exam fee), Certified Professional ($600/exam fee) and Delta Exam ($125/exam fee)
  • Tableau Server 10: Qualified Associate ($250/exam fee), Certified Professional ($800/exam fee) and Delta Exam ($125/exam fee)
Recurring/Renewal Costs
  • Renewal cost is equivalent to the fees paid annually, based on the number of users
  • Regular support services are built into the subscription price. However, professional services, such as on-site consulting, are add-on expenses
Cloudera
Cloudera
Start Price
$833
Per User, Annually
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Cloudera

Is Cloudera the answer to your data management woes, or is it just a bunch of hot air?

User reviews from the past year paint a mixed picture of Cloudera. While some users praise its flexibility and ability to handle large datasets, others find it cumbersome and expensive. Cloudera's hybrid cloud approach, allowing users to deploy on-premises or in the cloud, is a major selling point for many. However, some users find the platform's complexity a barrier to entry, especially for those without extensive experience in data management. Cloudera's integration with other tools, such as Apache Hadoop, is a key differentiator, but some users report issues with compatibility and performance.

Cloudera is best suited for large enterprises with complex data needs and a dedicated team of data engineers. Its robust features and scalability make it a powerful tool for organizations that require a comprehensive data management solution. However, smaller businesses or those with limited technical resources may find Cloudera's complexity and cost prohibitive.

Pros & Cons

  • Scalability: Cloudera can handle massive datasets and complex queries, making it suitable for large-scale data analysis and reporting.
  • Security: Cloudera offers robust security features, including data encryption and access control, ensuring sensitive data is protected.
  • Performance: Cloudera's optimized architecture and distributed processing capabilities deliver fast query execution and efficient data processing.
  • Integration: Cloudera integrates seamlessly with various data sources and tools, enabling users to connect and analyze data from different systems.
  • Community Support: Cloudera has a large and active community, providing access to resources, support, and best practices.
  • Steep Learning Curve: New users often find Cloudera's interface and complex architecture challenging to navigate, requiring significant time and effort to master. This can be especially problematic for teams with limited technical expertise.
  • Costly Implementation: Cloudera's pricing model can be expensive, particularly for large deployments. The cost of hardware, software licenses, and ongoing support can be a significant barrier for some organizations.
  • Limited Scalability: While Cloudera offers scalability, some users have reported challenges scaling their deployments to meet rapidly growing data volumes. This can lead to performance bottlenecks and slow query execution times.
  • Complex Management: Managing a Cloudera cluster can be complex, requiring specialized skills and knowledge. This can be a burden for organizations with limited IT resources.

Key Features

  • Data Science Workbench: Through a unified workflow, collaboratively experiment with data, share research between teams and get straight to production without having to recode. Create and deploy custom machine learning models and reproduce them confidently and consistently.
  • Real-Time Streaming Analytics: With edge-to-enterprise governance, Cloudera DataFlow continuously ingests, prioritizes and analyzes data for actionable insights in real-time. Develop workflows to move data from on-premises to the cloud or vice-versa, and monitor edge applications and streaming sources.
  • Machine Learning: Enable enterprise data science in the cloud with self-service access to governed data. Deploys machine learning workspaces with adjustable auto-suspending resource consumption guardrails that can provide end-to-end machine learning tools in one cohesive environment.
  • Data Warehouse: Merges data from unstructured, structured and edge sources. The auto-scaling data warehouse returns queries almost instantly and has an optimized infrastructure that moves workloads across platforms to prepare vast amounts of data for analysis.
  • Operational Database: The operational database promises both high concurrency and low latency, processing large loads of data simultaneously without delay. It can extract real-time insights and enable scalable data-driven applications. 
  • Open-Source Platform: Access the Apache-based source code for the program and make adjustments, customizations and updates as desired. 
  • Data Security and Governance: Reduce risk by setting data security and governance policies. The Cloudera Shared Data Experience (SDX) then automatically enforces these protocols across the entire platform, ensuring sensitive information consistently remains secure without disruption to business processes.
  • Hybrid Deployment: Leverage the deployment flexibility and accessibility to work on data wherever it lives. Read and write directly to cloud or on-premises storage environments. With a hybrid cloud-based architecture, choose between a PaaS offering or opt for more control via IaaS, private cloud, multi-cloud or on-premises deployment.
Domo
Domo
Start Price
$15
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Domo

Domo has everything data teams could wish for — self-service ETL, advanced analytics and data science. Its rich set of connectors makes users happy as they praise its robust integration APIs. Its scripting language is similar to Power BI, and knowing SQL will shorten your team's learning curve. The vendor offers daily refreshes, currently capping them at 48.

On the flip side, the interface seemed a bit clunky to me. Dashboards don’t display in the edit mode by default, which was a tad annoying. The Getting Started documentation is dated and doesn’t match the new interface. I could find my way around with help from user forums.

While the vendor earns praise for releasing frequent updates, quite a few users say some much-needed features lack depth. According to our research, Domo offers only 64% of the required functionality out of the box, which is much less than what Power BI and Tableau provide. It also underperforms in data querying, scoring only 53 in our analysis.

Some reviews mention bugs and that performance can lag when handling anything complex than simple visualizations. The slowness could be due to the multitenant SaaS model that provides shared computing. As for the mobile app, it didn’t work in the offline mode for me. I should mention here that I had opted for the trial version. A proof-of-concept will help you check if the issue persists in the paid edition.

Domo’s pay-as-you-go model is great for estimating usage but be prepared to pay more for workload spikes. According to our research, about 89% of users who reviewed the price found Domo’s consumption model expensive. Small organizations working with a lean team might find it challenging to handle billing.

Here’s what’s great about subscribing to Domo. You can create as many reports and dashboards as required — there’s no limit or additional cost. Plus, Domo allows adding an unlimited number of users. Domo accepts external data models from OpenAI, Amazon Bedrock, Hugging Face, Databricks and Jupyter Workspaces.

Despite a competitive market, Domo is an excellent product for organizations seeking data visualization and strong integration. Its flexible pricing model and recent AI updates make it a strong challenger to leading data platforms.

Pros & Cons

  • Source Connectivity: About 86% of users citing data integration said they could connect to their preferred sources easily.
  • Ease of Use: Around 82% of users discussing the interface said options and tabs were straightforward and intuitive.
  • Data Visualization: About 74% of people who reviewed Domo for graphics appreciated the ease of creating and sharing data stories.
  • Functionality: Around 73% of users who mentioned features said Domo performed as expected.
  • Support Services: About 71% of reviews discussing assistance praised the support team for being helpful and responsive.
  • Speed: About 78% of users discussing speed said the platform lagged sometimes.
  • Cost: Around 89% of users discussing price termed the platform as expensive.

Key Features

  • Domo App Studio: Design custom apps for needs Domo might not address out of the box. Build your own functionality without coding knowledge. Create branded visualizations with your trademark colors, logos and fonts. ESPN enhances the fan experience by capturing and analyzing customer data using a Domo Studio app.
  • Analyzer: Save time spent cleaning data manually. Use a special editor to set up checks for data inputs. Keep tabs on which charts and dataflows use a specific dataset with the lineage option. You can choose the best chart to present your data and annotate it. Use the Beast Mode for complex math.
  • DomoGPT: Get answers to data queries using AI Chat (currently in beta). Convert text to SQL or calculations and understand your data using text summaries. Use Domo.AI in a safe, governed space.
  • Personalized Data Permissions: Create custom data views for your users and hide sensitive data. Your regional managers get exclusive views specific to their roles, while senior management can switch between full and filtered views.
  • Domo Mobile: View cards and text summaries on the mobile app. Cards fit within the small screen, giving a great user experience. Domo Buzz allows sharing files to WhatsApp, Gmail, QuickShare and Google Drive. You can even save a screenshot to your phone gallery.
  • Alerts: Stay informed about KPIs that matter to you. Set new alerts and share them with selected users or subscribe to existing ones. Choose where you want to receive the notifications — email, mobile app or SMS.
Hortonworks
Hortonworks
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Deployment
Cloud On-Premise
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Key Features

  • Multi-Workload Processing: The product is able to handle multiple workloads and other taxing processes such as detailed analysis and report generation — all in parallel processes. 
  • Real-Time Processing: Users can take advantage of processing in real time, without having to wait for their data to finish compiling. 
  • Batch Processing: Batch processing is the processing of large quantities of data in large batches, significantly cutting down the time it takes to process information. 
  • Data Governance: Controlling, managing and distributing data are essential to a modern analytics solution. The software provides a suite of management features for users to take advantage of.  
  • Dataflow: Dataflow is an all-in-one data crunching feature that streams data and insights in real-time. It delivers actionable intelligence and curated data as it’s being processed. 
BIRT
BIRT
Start Price
$0
Open-Source
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked BIRT

Reviews for BIRT paint a picture of a user-friendly report designer with a loyal following, particularly among those new to report building. Its drag-and-drop interface and pre-built templates are praised for streamlining report creation compared to coding-heavy solutions. This is a major advantage for businesses that need reports up and running quickly, without tasking developers.

BIRT shines in its ability to connect to various data sources, eliminating the need for complex data extraction steps. This is a big win for teams working with data scattered across spreadsheets, databases, and even flat files. While BIRT offers strong core functionality, some reviewers mention a steeper learning curve for advanced features. For instance, users who need to wrangle massive datasets or create highly customized data visualizations might find BIRT limiting compared to more feature-rich (and often pricier) competitors. Similarly, BIRT's mobile capabilities are seen as less robust than some competing products. This can be a dealbreaker for teams that prioritize mobile reporting for field staff or remote workers who need real-time access to reports.

In conclusion, BIRT is a solid choice for businesses seeking a cost-effective and user-friendly solution for generating basic to moderately complex reports. Its strength lies in its ease of use, data source flexibility, and affordability. However, those working with exceptionally large datasets or requiring a highly customized mobile reporting experience might be better served by exploring feature-rich alternatives.

Pros & Cons

  • Drag-and-Drop Report Design: BIRT boasts a user-friendly interface that lets you visually design reports using drag-and-drop functionality. No coding required! This streamlines report creation for business users who might not be familiar with programming languages.
  • Flexibility and Customization: BIRT empowers you to tailor reports to your specific needs. From basic tables to complex charts and graphs, BIRT offers a wide range of data visualization options. This ensures your reports effectively communicate insights to a variety of audiences.
  • Integration with Various Data Sources: BIRT seamlessly connects to diverse data sources like databases, spreadsheets, and even flat files. This eliminates the hassle of data extraction and manipulation, allowing you to focus on crafting impactful reports.
  • Scheduling and Automation: BIRT allows you to schedule reports to run automatically at specific intervals. This ensures you always have access to fresh data, saving valuable time and keeping everyone on the same page.
  • Steeper Learning Curve for Advanced Features: While BIRT excels in drag-and-drop simplicity for basic reports, users venturing into complex functionalities like data scripting or advanced data manipulation might find the learning curve steeper. This can be a hurdle for teams without in-house BIRT expertise.
  • Potential Performance Issues with Large Datasets: Some users report performance limitations with exceptionally large datasets. BIRT might not be the ideal choice if you consistently work with massive amounts of data that require real-time report generation.
  • Limited Mobile Capabilities: While BIRT reports can be accessed on mobile devices, the user experience might not be optimal for all report formats. This can be a drawback for teams that heavily rely on mobile reporting for on-the-go decision-making.

Key Features

  • Data Explorer: Build connections to data sources and view them together with business assets and data cubes.
    • Access and blend various datatypes from a range of sources, including POJOs, JDO datastores, SQL Databases, JFire scripting objects, XML and web services. 
    • Pull in more data than before by extending the available sources with Eclipse’s Open Data Access framework. 
    • Accesses Hadoop data using Hive Query Language. Ingests data from distributed databases like Cassandra through APIs. 
    • Pulls data from LDAP, report documents and Java objects. In addition to Salesforce, it can ingest information from LinkedIn, Facebook and GitHub. 
  • Report Designer: Supports a wide range of reports, layouts and formatting, with reuse of designs and elements. Create report layouts, connect to data sources and produce XML-based report designs. 
    • Navigator: Create a new Report Design Project or a new BIRT file within a project through the navigator. View all the projects in the workspace and create multiple file types such as a design file, template file, library file or report document. 
  • Sample Report Viewer: View reports in a sample web application before publishing them online. Generate and export them in HTML, PDF, CSV formats. Print locally or on the server and rerun them when needed with new parameters. 
  • BIRT Report Engine: Query data sources and merge the query results into the report layouts created by the Report Designer. Produce the report output in a wide range of formats that include Microsoft Office, HTML, PDF, postscript and open document formats. This feature is also available in the BIRT Web Viewer. 
  • Data Visualizations: Create data visualizations with task-specific editors, builders and wizards and integrate into web systems. 
    • Charts: Choose from a vast library of pie charts, Gantt charts, pyramid charts, scattergrams and many more, with multiple subtypes, such as a bar chart with options of side-by-side, stacked and percent stacked. Create charts in 2D, 2D with depth and 3D formats. 
    • Crosstabs: Present data in two dimensions (sales or hits) with the cross-tabulation or matrix feature. 
    • Palette: Drag and drop elements from the palette into the visualization layout. Add rich text to the report, including HTML formatting integrated with dynamic data. Aggregate business metrics with more than 30 built-in SQL operators. 
  • Customization: Make report data easy to understand with support for internationalization, including bidirectional text. Easily replace static report elements — report labels, table headers and chart titles — with localized text. 
  • Lists: Present data methodically through simple data lists by grouping related data together. 
Zoomdata
Zoomdata
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Zoomdata

How can Zoomdata help your business intelligence "zoom" to new heights? Zoomdata (now known as Logi Composer) is a business intelligence tool that garnered a devoted following for its ability to handle large, complex datasets, making data visualization and analysis accessible to a wide range of users. Users rave about its intuitive interface and real-time streaming data analysis capabilities, allowing them to "pause, rewind, and replay" data streams, a feature described by one user as a "game-changer" for identifying patterns and anomalies. This sets it apart from traditional BI tools that often lag in real-time data processing.

However, Zoomdata is not without its drawbacks. Some users have pointed out the absence of built-in predictive analytics features, limiting its ability to forecast future trends and perform more in-depth statistical modeling. For instance, one user noted that while Zoomdata excels at presenting "what is happening," it lacks the robust forecasting tools found in competitors like Tableau, making it less suitable for businesses heavily reliant on predictive modeling.

Overall, Zoomdata (Logi Composer) is best suited for organizations seeking a user-friendly BI tool for real-time data visualization and exploration, particularly those dealing with large, streaming datasets. Its intuitive design and powerful data sharpening capabilities make it ideal for users of all technical levels, empowering them to make data-driven decisions quickly and efficiently. However, businesses requiring advanced predictive analytics or statistical modeling features may need to consider alternative solutions or integrations to supplement Zoomdata's capabilities.

Pros & Cons

  • Fast Visual Analysis: Zoomdata is known for its ability to quickly process and visually analyze large datasets, making it ideal for handling big data.
  • Easy-to-Use Interface: Zoomdata offers a user-friendly platform that allows users with varying technical skills to explore data, identify trends, and generate visualizations without relying on data analysts.
  • Seamless Integration: Zoomdata can be easily integrated with other platforms, such as ClickFox, to enhance customer journey analysis by providing interactive dashboards and actionable insights.
  • Lack of Mobile App: Zoomdata lacks a dedicated mobile application, which can be a drawback for users who need to access data and dashboards on the go.
  • No Version Control: The platform lacks version control features, making it difficult to track changes to dashboards and analyses over time, potentially leading to confusion and difficulty in reverting to previous versions.

Key Features

  • Data Connectors: Users have access to a suite of pre-designed connectors that draw data directly from their selected data source. 
  • Drag-and-Drop Attributes: The simple drag-and-drop interface lets users drag, pinch, zoom, swipe and drop to create dashboards. 
  • Data Exploration: The system allows users to create interactive visualizations, customize dashboards and perform self-service analysis to discover data insights. 
  • Data DVR: By unifying historical data analysis with real-time data into a single interface, the dashboards operate like a video, allowing users to pause, rewind, fast forward and replay data streams. 
  • Microservices: Taking advantage of small, coupled programs that work in tandem with one another, Zoomdata is able to scale with a user’s business needs. Microservices are written to be able to be deployed and restart on-the-fly in the event of an outage. 
Alteryx
Alteryx
Start Price
$99
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Alteryx

Alteryx is a data science solution that leverages the power of AI and ML to blend, parse, transform and visualize big business data to promote self-serve analysis of business metrics.
Many users who reviewed data analysis said that the tool performs statistical, spatial and predictive analysis in the same workflow. Most of the users who reviewed data processing said that, with a lightweight ETL tool, the platform has strong data manipulation and modeling efficiencies, though some users said that it can be tricky to use SQL queries. Citing integration with Power BI, Tableau and Python, most of the users said that the tool connects seamlessly to data from databases and files, apps, and third-party data sources, among others, to expand the reach of search-based and AI-driven analytics. Most of the users who discussed ease of use said that the tool is intuitive with drag-and-drop functionality and a well-designed interface, though some users said error handling can be challenging for automated workflows. Most of the users who reviewed support said that online communities are helpful in providing answers to queries. Citing automated workflows, many users said that the tool helps save time, though some users said that these can be overly complex and need improvement.
On the flip side, many users who reviewed pricing said that its expensive licenses and add-ons are cost-prohibitive, and cost per core is high for enterprises looking to scale. A majority of users who reviewed its visualization capabilities said that they need to export data to visually stronger applications, such as Tableau or Power BI, to make the reports presentation-worthy. Citing slow runtimes when executing complex workflows, especially with large datasets, many users said that performance-wise, the solution is prone to infrequent crashes. Most of the users who discussed learning said that with documentation not being in sync with latest releases, training is a must to optimally use the tool.
Overall, Alteryx is a data science tool that, with its low-code approach and strong data wrangling capabilities, makes the journey from data acquisition to data insights seamless and promotes data literacy across organizations, though it might be better suited for medium- to large-sized organizations.

Pros & Cons

  • Data Analysis: All users who reviewed analytics said that the platform adds value to data through features such as statistical modeling and predictive analysis.
  • Data Processing: Around 86% of the users who mentioned data processing said that, with a lightweight ETL tool, the solution excels at data wrangling for further analysis.
  • Data Integration: Citing strong integration with multiple data sources and tools, around 84% of the users said that it works well with big data.
  • Ease of Use: Approximately 83% of the users who mentioned ease of use said that the platform’s low-code approach, with drag-and-drop functionality, makes the interface user-friendly.
  • Online Community: The online community is responsive and helpful, according to around 74% of users who discussed support for the platform.
  • Functionality: With fuzzy matching and join capabilities, the platform is feature-rich and versatile, said approximately 63% of users who discussed functionality.
  • Cost: In addition to the high cost of licenses, the price of add-ons is limiting, said around 89% of the users who reviewed pricing.
  • Data Visualization: Around 75% of users who reviewed its presentation capabilities said that with outdated graphics, the platform lags behind other solutions in data visualization.
  • Performance: The solution is prone to infrequent crashes, especially when processing large amounts of data, as said by 65% of users who discussed performance.
  • Training: Approximately 54% of the users who reviewed learning said that with the documentation not being up to date with latest features, there is a steep learning curve and training is required.

Key Features

  • Internal Data Visualization: Display data insights at each stage of ETL, enabling validation and verification at every step of analysis through its in-platform data visualization solution, Visualytics. 
  • Data Visualization Export: Export to data visualizers like Qlikview and Tableau in several formats seamlessly, if the platform’s in-house visualization capabilities don’t satisfy the business’s needs. 
Spotfire
Spotfire
Start Price
$0.99
Monthly, Quote-based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Spotfire

In online reviews, Spotfire emerges as a user-friendly big data platform. Most users found data exploration easy with a drag-and-drop interface. Some users said the UI was dated, though, and said it could use a revamp. Most users praised its interactive visualizations and dashboards, saying they helped them interpret data better. But, a few said they would love to have more visuals to choose from.

A user mentioned they did the calculations in Excel and imported them into Spotfire for visualization. It's a common scenario when a steep learning curve slows down adoption, and teams fall back on Excel. Most users said Spotfire takes time to learn. You might have to opt for a balance of multiple platforms to balance your departmental and enterprise needs.

Spotfire surpasses Excel in data management, especially data prep. Customizable visualizations and custom Mods give you enough freedom to work within the platform.

Though 72% of reviewers were happy with the integrations, Spotfire lacks some standard connectors, such as for Apache Kafka, forcing users to rely on workarounds.

A majority of users found its pricing structure complex, especially as users increased. In such cases, organizations often tend to opt for a cheaper alternative for less advanced use cases while using the pricier platform for the critical ones. We advise doing a deep dive into the vendor's pricing plans to avoid making your tech stack top-heavy.

Ultimately, Spotfire's appeal lies in its balance. It's visually captivating and user-friendly for casual users while offering enough depth for seasoned analysts. However, its pricing and learning curve might deter organizations on a tight budget.

Pros & Cons

  • Data Visualization: About 86% of reviewers were satisfied with the available options when designing dashboards.
  • Support: Around 74% of users praised vendor support for their timely response and helpful attitude.
  • Integration: Almost 72% of users were satisfied that it integrates with their preferred systems.
  • Friendly Interface: Around 68% of reviewers said the platform was easy to use.
  • Functionality: About 64% of users said it had a rich feature set.
  • Cost: Around 96% of the user reviews said it the price was high and licensing complex.
  • Adoption: 90% of reviewers said there was a significant learning curve and users would need specialized knowledge of data science and statistics.

Key Features

  • Spotfire Actions: Decide what to do with and act instantly — no need to switch to your procurement application to pause new orders. This powerful feature allows you to run scripts within analytics workflows. You can also trigger actions in your external system through visualization. Spotfire can set up over 200 commercial connections and has 1800 community connectors.
  • Mods: Build reusable workflows and visualization components, much like apps in Power BI and Qlik Sense. They allow your users to tailor their analytical processes so they don’t have to start from scratch every time. Based on code, they run in a sandbox with limited access to system resources for security. Users can share them through the Spotfire library. Mods improve efficiency and collaboration.
  • Batch Edits: Make similar changes to multiple files in one go. Write custom scripts to call the Spotfire API that’ll make changes to the files. Update the IronPython version to the latest one or embed the Spotfire JQueryUI library instead of its references.
  • Recurring Jobs: Simplify event scheduling to better manage your time and tasks. Improve efficiency and deliver reports at the same time on the same day of the week or month. The latest Spotfire version allows you to set recurring automation jobs to occur every X hours, days, weeks or months.
  • Web Player REST API: Share insight with clients and partners without them needing to sign up for a paid Spotfire account. Engage them via data visualizations on the web browser, thanks to Spotfire Web Player. Update analyses on the web with real-time data in the latest Spotfire version.
  • Roles: Invest wisely — opt for licenses that align with user roles. Choose Spotfire Analyst for data analysts, scientists and power users who need deep-dive analysis. Get the Business Author license for enterprise users, analysts and power users to create and consume insights without deep expertise. Choose consumer licenses for users who’ll interact with and consume data. They include the C-suite and non-technical users within the organization.
  • Information Designer: Prepare fully governed data sources for business users in a dedicated wizard. Set up their preferred data sources and define in advance how Spotfire will query and import data into storage. Specify which columns to load and which filters, joins and aggregations to apply.
  • Audio and Image Processing: Add user feedback from customer calls and videos. Interpret public sentiment about your product by analyzing social media pictures and videos. Spotfire enables writing code to extract text from audio and image files. You can then import the data into the platform for analysis.
  • IoT Analytics: Gain insight at lightning speed; build microservices and deploy them at the edge. With Spotfire, you can add IoT data to your regular data for the complete picture.
BigQuery
BigQuery
Start Price
$6.25
Per TiB, Usage-Based
Company Size
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Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked BigQuery

Bigquery is a scalable big data warehouse solution. It enables users to pull correlated data streams using SQL like queries. Queries are executed fast regardless of the size of the datasets. It manages the dynamic distribution of workloads across computational clusters. The easy-to-navigate UI is robust and allows the user to create and execute machine learning models seamlessly. Users liked that it can connect to a variety of data analytics and visualization tools. However, users complained that query optimization is an additional hassle they have to deal with, as the solution is expensive and poorly constructed queries can quickly accumulate charges. It can be overwhelming for the non-technical user, and SQL coding knowledge is required to leverage its data analysis capabilities. Data visualization features are lacking and in need of improvement.

Pros & Cons

  • Performance: The system can execute queries on massive amounts of data with agility, as specified by about 89% of users who mentioned performance.
  • Functionality: About 68% of users who reviewed functionality talked about its robust inbuilt features.
  • Ease of Use: The UI is simple and easy to navigate, according to about 72% of users who talked about user-friendliness.
  • Integration: Approximately 75% of reviewers who talked about integration said that it connects to numerous other tools seamlessly.
  • Scalability: All users who reviewed scalability said that the platform scales to thousands of servers.
  • Cost: Approximately 76% of users who mentioned cost complained that it’s expensive, and charges can rack up quickly if queries aren’t properly constructed.
  • Learning Curve: About 82% of users mentioned that the software has a steep learning curve.
  • Resources: About 89% of users who spoke about resources said that documentation and video tutorials are lacking and need improvement.
  • Visualization: Data visualization capabilities aren’t up to the mark, according to all users who talked about visualization.

Key Features

  • Machine Learning: Comes with machine learning modules that can perform mass-segmentation and recommendations in seconds. These modules can be built and trained within minutes without ingesting data for training. 
  • Cloud Hosted: Handles all the hardware provisioning, warehousing and hardware management from the cloud. 
  • Real-Time Analytics: Large volumes of business data are quickly analyzed and presented to the user to ensure that insights and data discrepancies can be immediately uncovered. 
  • Automated Backups: Data is automatically stored and backed up multiple times a day. Data histories can be easily restored to prevent loss and major changes. 
  • Big Data Ecosystem Integrations: Integrate with other big data products such as Hadoop, Spark and Beam. Data can be directly written from the system into these products. 
  • Data Governance: Features such as access management, filter views, encryption and more are included in the software. The product is compliant with data regulations such as the GDPR. 

COMPARE THE BEST Big Data Visualization Tools

Select up to 2 Products from the list below to compare

 
Product
Score
i
SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Big Data Visualization Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Start Price
Free Trial
Company Size
Deployment
Platform
Logo
Hadoop
Undisclosed
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hadoop
Tableau Big Data
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Tableau Big Data
Cloudera
$833
Per User, Annually
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Cloudera
Domo
$15
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Domo
Hortonworks
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hortonworks
BIRT
$0
Open-Source
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
BIRT
Zoomdata
Undisclosed
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Zoomdata
Alteryx
$99
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Alteryx
Spotfire
$0.99
Monthly, Quote-based
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Spotfire
BigQuery
$6.25
Per TiB, Usage-Based
Yes
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Cloud On-Premise
Mac Windows Linux Chromebook Android
BigQuery

All Big Data Visualization Tools (52 found)

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Apache Pig

by Apache Software Foundation
Apache Pig
Apache Pig is an open-source platform ideal for analyzing large datasets and representing them through data flows. Leveraging its high-level language, Pig Latin, users can develop functions and write scripts of their own to process data. Using a component known as Pig Engine, these scripts are further transformed into MapReduce tasks. With the help of a multi-query approach, users can easily reduce the code’s length, thus, decreasing development time. It’s commonly used with Hadoop to perform all aspects of data manipulation.
Cost Breakdown
$10 - $100
Company Size
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Deployment
Cloud On-Premise
Platform
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Logi Symphony

by insightsoftware
Logi Symphony
Logi Symphony (formerly Logi Analytics) provides businesses with interactive data analytics and visualizations, offering advanced reporting and visualization features in a minimal-code environment. It offers self-service data analysis and data discovery; then connects and blends operational intelligence to prepare it; and finally lets users turn it into dashboards, reports and other visualizations.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
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Cloud On-Premise
Platform
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Striim

by Striim
Striim
Striim is a data integration platform that facilitates ingestion, processing and delivery of real-time data across diverse cloud or on-premise environments. It ingests high-volume and high-velocity data from enterprise databases, log files, messaging systems, cloud applications and more. It performs on-flight data processing, including filtering, aggregations, transformations, masking and data enrichment. It offers built-in pipeline monitoring and validation capabilities. Its distributed clustered architecture enables users to scale horizontally to cater to data processing needs.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$100 - $500
Company Size
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Deployment
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Platform
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Oracle Analytics Cloud

by Oracle America
Oracle Analytics Cloud
Oracle Analytics Cloud is a public cloud service on which you can build and run reporting and analytics solutions. It can also manage data from Oracle Database and Oracle Autonomous Data Warehouse. Pairing it with Oracle Data Integration can help you move and transform data at scale. But that’s not all. You can build and deploy analytics applications even with non-Oracle systems. However, the level of integration and capabilities may differ.You’ll need an Oracle Cloud account, and a free trial is available. Oracle customizes the view for you, whether you’re a web app developer, account admin or cloud architect. Workbooks and dashboards enable data exploration. You can set it up so the system sends reports to selected users on schedule. Governed analytics is available; your data stays secure. Annotations, export options and integration with social platforms are other capabilities.Two pricing tiers are available. The Professional edition has self-serve analytics, direct source connections and dataflows for preparing content. It costs $16 per user monthly.The Enterprise edition lets you enrich data, connect to private sources and build semantic models. You can track usage and set encryption keys, all at a cost of $80 per user monthly. Opt for a user or consumption model based on OCPUs (Oracle CPUs).The user reviews were an interesting read. I haven’t seen many products with such strongly divided opinions. In all positive reviews, over 70% of the users voted for a specific feature, indicating a high level of user satisfaction and perceived value. On the flip side, over 87% of users said price and a steep learning curve were blockers to successful adoption.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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DBSync

by DBSync
DBSync
DBSync is an integration Platform-as-a-Service (iPaaS) that provides bi-directional data synchronization, migration and replication to enterprises. It enables businesses to connect to any combination of SaaS, cloud and on-premise applications and databases. It empowers companies to build warehouses for reporting and data mining. Its powerful integration engine offers scheduling, logging and transformation to streamline data management.Easily accessible from any device, it can be deployed in the cloud, on-premises or as a hybrid. In addition to its free Standard version, the vendor also offers paid pricing plans.
Cost Breakdown
$100 - $500
Company Size
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Apache Phoenix

by Apache Software Foundation
Apache Phoenix
Apache Phoenix is an open-source and relational database engine that uses SQL interface over HBase. With the standard JDBC APIs, users can easily create, delete, and alter SQL sequences, as well as delete or insert rows for tables in a single format or bulk. Via the HBase API, custom filters and coprocessors, it can load and query tens of thousands and even billions of records.
Cost Breakdown
$100 - $500
Company Size
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Deployment
Cloud On-Premise
Platform
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GridGain

by GridGain Systems
GridGain
Gridgain speeds up and scales data-demanding applications and provides real-time data processing through its distributed in-memory computing architecture. It is built on Apache Ignite to enable developers to modify its functionality without altering source codes. It is deployed securely across numerous global data servers to power existing or new data-critical projects with zero downtime. Customers can utilize robust integrations with relational operational databases (RDBMS), NoSQL databases and data lakes. In addition, it offers data monitoring, visualizations, off-heap memory support, load-balancing, fault tolerance and remote connectivity.The vendor offers three packages including an open-source community edition along with enterprise and ultimate packages with varied modules and cloud, on-premises and hybrid deployment options. Companies use it to meet the real-time big data demands of a variety of use cases such as ACID transactions, hybrid transactional/analytical processing (HTAP), ANSI-99 SQL, accelerated query processing, streaming analytics and machine learning frameworks.
Cost Breakdown
$100 - $500
Company Size
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Deployment
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InPhase

by INPHASE LIMITED
InPhase
InPhase is an end-to-end scalable platform that enables organizations to make the most out of business data, improve decision-making and troubleshoot issues. It generates and delivers easy-to-interpret data insights to relevant stakeholders faster through dashboards and automated routing. Organizations can avert crises, anticipate outcomes and prevent wasted efforts using robust KPI monitoring and predictive analytics. Easy to customize, use and scale, it offers optional ready-to-use packages to facilitate various business processes. It also provides data consistency, mobility, communication and team planning modules to enable remote collaboration across devices. The vendor provides onboarding in days and offers extensibility through development to tech-savvy users.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 - $100
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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SlamData

by SlamData
SlamData
SlamData is an open source tool based on SQL that aims to make it easy for developers and non-developers to access MongoDB. This software is one of the fastest growing products in the NoSQL database space. This program brings high-quality source integration to the table, meaning that users can import from any source; no coding skills needed. This data can then be streamed to a variety of sources.
Cost Breakdown
$100 - $500
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Upsolver

by Upsolver
Upsolver
Upsolver is a big data solution for ingesting and maintaining data at scale. It delivers exactly-once, strongly-ordered data in real time. It can ingest data from a variety of database types and formats. It identifies and addresses data quality issues during the ingestion process to avoid downstream errors and can automatically adjust for upstream schema changes. It uses cloud infrastructure and can deploy to AWS on a virtual private cloud. Processes can be configured through either SQL or an integrated development environment.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$500 - $1,000
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Microsoft Machine Learning Server

by Microsoft Corporation
Microsoft Machine Learning Server
Microsoft Machine Learning Server is an AI-enabled enterprise intelligence solution with big data capabilities. With full R and Python support, it produces predictive and retrospective analytics, with the ability to score structured and unstructured data. It is fully machine learning enabled, with options to train or use prebuilt trained models. It integrates with major open-source resources and other Microsoft entities like Azure. Its web service publishing and operationalization features streamline putting insights to work.
Cost Breakdown
$100 - $500
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Dundas BI

by Dundas Data Visualization
Dundas BI
Dundas BI is a self-service analytics solution with a keen focus on embeddability. With an eye on the end-user experience, the vendor provides options to design and embed dashboards into applications. Ranked five on our leaderboard, Dundas BI is a user favorite. The platform supports standard file formats and connects to cloud storage and enterprise systems.Report templates and interactive visualizations make data accessible to all users. Some available graphics include bar and line graphs, scattergrams, pie charts, scorecards and maps. The system also suggests visualizations that fit the analyzed data.Besides a long list of connectors, the platform has built-in ETL. You can switch from a data warehouse to live sources with the click of a button and write back to the warehouse from the interface.The vendor, insightsoftware, offers it under the Logi Symphony umbrella, but Dundas BI remains a distinct product, and support is available. It supports multi-tenancy, and you can manage multiple clients with assured data security.Roles include developers, power users and report consumers. The vendor provides shared concurrent licenses to keep pricing within reach. Unlimited data refreshes at no extra cost make it an attractive option.Reviews praise its visual capabilities and ease of use, though most users say there’s a steep learning curve and performance can lag sometimes. Pricing starts from $2,500 monthly.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
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Deployment
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Platform
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Buyer's Guide

Big Data Visualization Tools Are All About Cognitive Business Intelligence

By Ritinder Kaur, Market Analyst at SelectHub

Big Data Visualization Tools BG Cover

Big data analytics tools use graphics well, as human cognition is better with visuals. Visualization platforms serve the last mile in data interpretation, bringing users closer to valuable insight.

Extensive source connectivity and interactivity are two features to look for when seeking big data visualization tools. What else should you keep in mind? In this buyer’s guide, we’ll discuss their definition, primary features and other buying considerations.

Executive Summary

  • Big data visualization tools are software programs that display information through computer graphics for easy interpretation.
  • Discoverability, integrations and security are some features to ask for when seeking new software.
  • Meet with vendors to ask questions and clarify doubts before purchasing a solution.
What This Guide Covers:

What Are Big Data Visualization Tools?

Big data visualization tools are software programs for showing information with computer-based graphics.

You don't need an understanding of mathematics or statistics to interpret visualizations. They work great for massive amounts of data that are difficult to analyze manually. Their usability lies in uncovering helpful metrics while hiding irrelevant information.

Big data refers to large, complex information volumes of disparate types. Organizations with a digital footprint have information spread across many mediums.

Why We Need Them

Aggregating and analyzing data from websites, cloud sources and transactional systems is complicated. It can become too complex for conventional BI tools and Google Charts. Streaming and IoT (Internet of Things) data add to the mix.

Here’s why other software might not be the best for data visualization.

  • Qualitative analytics tools capture non-numeric information, a lot of which is visual. But they need robust app integrations to show the complete picture.
  • Data mining helps extract raw data but doesn’t provide the means to understand it. Data mining models aren’t easy to interpret as they flatten the information into two-dimensional tables. This one-size-fits-all approach is opaque and unhelpful.
  • BI, big data analytics, enterprise reporting and dashboard systems are great visualization platforms. But, they may not have the features, flexibility and integrations as dedicated tools. Read more about it in the FAQ section.

Visualization platforms pick up from where data mining leaves off. Dedicated visualization tools include Python and Javascript libraries like Plotly and D3.js.

The need for real-time insight drives the popularity of these tools. A good visualization tool must focus on answering data queries. It should be the focus of your software search.

Big Data Visualization Tools Categories

Allied Market Research predicts the data visualization software market will grow to $19.5 billion by 2031.

Big Data Visualization Tools Market Summary Chart

 

 

 

Most Popular Big Data Visualization Tools

Check out the most popular visualization platforms in the market.

Tableau

It’s an interactive visualization tool with an intuitive interface and several graphics options. It connects to on-premise, server and cloud sources, including Google Analytics, Salesforce and Amazon Redshift.

Features
  • With its Ask Data feature, you can query and interact with data.
  • The Show Me functionality recommends visualizations based on the selected data.
  • Role-based access control (RBAC) is available.
  • Explain Data is an AI (artificial intelligence) functionality to convey data associations and make suggestions in plain text.
  • Tableau Desktop is free for students.

Tableau

A sales comparison of the last quarter with 12 previous quarters. Source

Pros
  • It’s user-friendly and enjoys a mass fan base with students, amateurs and industry professionals connecting via online communities and contests.
  • It’s a powerful tool with best-in-industry data visualizations.
  • Tableau has self-service BI capabilities.
  • It enables insight sharing with web accessibility.
  • The vendor provides excellent customer support.
Cons
  • It doesn’t perform well when loading complex visualizations.
  • It doesn’t provide flexible pricing and has a high subscription cost.
  • The platform has a steep learning curve.

Free Trial? Yes

Board

It’s a self-service visualization platform known for EPM (enterprise performance management), predictive and business analytics and data discovery. The platform offers multi-language support and HTML5 web technology.

Features

Board

A revenue dashboard with a donut chart and histogram. Source

Pros
  • The platform has a no-code, user-friendly interface.
  • You can automate workflows.
  • The solution serves BI and EPM needs.
  • It has robust functionality.
Cons
  • There’s a steep learning curve.
  • The platform isn’t compatible with Mac devices.

Free Trial? No

Domo

It’s a visual analytics platform that deploys on Mac, Windows and Linux, serving enterprises of all sizes. Domo has a rich visualization library and a built-in collaboration tool, Domo Buzz. Regular updates incorporate users’ feature requests.

Features
  • The solution has visual ETL (extract, transform and load) workflows.
  • The vendor provides natural language processing (NLP) capabilities.
  • Configuring email alerts is possible.
  • You can set dashboards to refresh automatically at preset intervals.

Domo

Visualization platforms aggregate diverse domain data on one screen. Source

Pros
Cons
  • Many necessary features are available only through add-ons.
  • The platform doesn’t have unlimited geospatial visualization capabilities.
  • There’s a steep learning curve.

Free Trial? Yes

Primary Benefits

EMR, HR, accounting and project management have many visualization use cases. Visualizations are a boon for decision-makers, answering questions with clear and concise graphics.

Big Data Visualization Tools Benefits

  • Gain business intelligence by viewing critical metrics as simple, clear, concise graphics. You can understand how the business performs and think of ways to improve.
  • Identify business trends by associating datasets with highlighting and brushing.
  • Promote employee engagement by enabling them to interpret data independently. Interactivity promotes shared ownership of project goals.
  • Boost productivity with ready information and quick turnarounds. It removes dependence on data analysts and encourages independent decision-making.
  • Manage big data from collection to usage and archival with your visualization platform.

Key Features & Functionality

Before embarking on your software search, list your must-haves and nice-to-have features. It can help you sift out the products missing key functionality but promise you the moon.

Big Data Connectivity

Connectivity to files, websites, cloud services and other analytics and BI tools is essential.

A Hadoop integration can be a lifesaver when dealing with complex workloads.

JDBC and ODBC connections enable interfacing with external databases. Ask if the software can display streaming data as it comes in, if applicable to your company.
Integrations

Your big data visualization platform is only as good as its integrations. Combining your CRM, POS and inventory platform data gives you the complete picture.

Applications that slow down when loading and processing information can harm business. A trial run of the software can help you check it.

Changing information in visualizations should not impact the underlying databases. The platform should integrate with Javascript and Python libraries for complex visualizations.
Visualizations

Check with vendors if the platform provides your preferred visualizations. Ask for 3D graphics if you need those.

Visual elements like animations show changes across periods, driving professional presentations.

Automatic data refreshes are essential for the latest updates. But, performance can lag if the platform struggles to sync data updates with visuals.

Auto-charting should be built-in, recommending the best visualization for the selected datasets.
Dashboards

A single-screen view of KPIs in individual panels is helpful for fast insight. Check if it’s responsive to user actions like point-and-click and drag-and-drop.

Branding helps teams identify which project they're working on and gives clients a familiar interface.

Dashboards should embed into reports, websites and applications. Mobile compatibility is a standard business need, so check if it’s a deal-breaker.
Interactivity and Advanced Analytics

Static visualizations are helpful, but customizable graphics let you view the desired metrics.

They change the narrative from descriptive to diagnostic, predictive and prescriptive analytics.

Data exploration drives problem-solving. It gives you the power to predict what’s likely to happen.

Additionally, you can devise possible courses of action using machine learning algorithms. What-if analysis involves changing the variables to see their impact.

Regression analysis involves configuring the desired outcome and going back to derive the defining values.

It helps you change your approach today to get the desired results tomorrow.
Data Governance and Security

Following data privacy regulations like the GDPR (General Data Protection Regulation) is necessary.

The vendor must follow HIPAA (Health Insurance Portability and Accountability Act) rules for medical software.

Authentication protocols like SSO (Single Sign-on) and SAML (security assertion markup language) are essential for seamless access.

Administrators must have the power to restrict access based on user roles. Activity logs and audit workflows promote accountability.
Shareability

Converting visualizations into reports makes them shareable via downloads or email. For people who don’t have access, shareable URLs should enable viewing reports as guests.

Collaboration with shared comments can help in meaningful discussions around data. Versioning retains previous copies, enabling you to play around with data.
Discoverability

Your users should be able to discover what you can do. The learning curve shouldn’t be steep.

Complete command suggestions, as in Tableau’s Ask Data functionality, help users scale quicker. Text summaries of query results are easy to understand.
Responsiveness

Robust data visualization tools support real-time conversations between you and your data. User actions should evoke an equally fast response from the software.

The system must retain a memory of the query behind the current visualization and then change it to answer the latest question.

Cost & Pricing Considerations

Where does your existing solution fall short, and what needs should the new one address? Assess your business requirements to get the best data visualization tool. How many employees will use the program? Will they need training?

Software costs can vary depending on

  • User capacity
  • Storage and processing consumption
  • Feature set
  • Data migration needs
  • Customizations
  • Add-ons

Consider this.

  • Acquiring on-premise software can cost you a hefty sum, but it’s one-time. Maintenance and updates are on you.
  • SaaS (Software-as-a-Service) models are subscription-based and may offer per-user licensing or usage-based packs. Costs include installation, maintenance and basic support.
  • Customization costs may vary depending on functional requirements. These include operational reporting, dashboard configuration, workflow complexity and UI modification needs.
  • Data migration costs depend on the system gaps, data volume and complexity.

Opting for a bare-bones solution to stay within budget might pinch in the future. What tradeoffs are you willing to accept? Will scaling the software cost you the same as the price of an advanced solution today?

The volume, variety, velocity, veracity and value of big data are critical factors. Check the following while evaluating software.

  • A robust code is the most critical aspect of a software solution. Processing diverse data points can reduce performance. Is the interface responsive — does the data load instantly, or is there a lag?
  • Latency is a common issue when mapping user actions to visuals. Increased load and refresh times make for a bad user experience.
  • Big data has thousands of interlinked data sets, and isolating a few is hard work. It can impact performance, so check it during the trial run.

Parallelization can resolve the above issues. Confirm whether the visualization tool works with Hadoop, Spark, and cloud-based servers.

Don’t miss out on vendor research. Use every source of information available — online reviews, industry guides and colleague recommendations. Consider opting for a free trial.

Check the product price from vendor websites or contact them directly for a quote.

Wondering how to begin your software search? Get a head start with our readymade requirements template.

Questions To Ask

To get a better understanding of your requirements, ask these questions internally.

  • Which are our preferred data sources?
  • What gaps do we hope to fill with the new system?
  • Who will use the solution, and what’s their average skill level?
  • Which features are must-haves?
  • Do we have the technical resources, or will we need vendor support?

Big Data Visualization Tools Key Questions To Ask

Ask queries and address any concerns during vendor meetings. Here are some questions to add to your list.

  • How does the solution show time-series data?
  • How does it manage incomplete data and missing values?
  • Is there any impact on the display resolution when visualizing multivariate data?
  • How many years of expertise do you have in the industry?
  • What company sizes do you work with?

FAQs

1. Is Microsoft Power BI a data visualization tool?

Yes, it’s a BI and analytics tool. Power View is its data visualization technology that works with Power Query and Power Pivot modules. Initially an Excel add-on, it now claims its separate market share.

Power BI connects to several sources outside the Microsoft family. The vendor offers rich visualizations and enables embedding them into other applications.

2. Is Microsoft Excel a data visualization tool?

Excel by Microsoft is a spreadsheet tool for mathematical calculations and statistical analysis. It’s a powerful visualization platform but slows down when processing large data sets.

Organizations rely on Excel for calculations despite using BI and analytics tools. It interfaces with many applications, so it's popular with users.

Read our article on Excel and Business Intelligence to learn about major players offering stiff competition.

3. What are some free graphics-based analytics tools?

Tableau Public, Power BI Desktop, Google Data Studio and Chartbuilder are some examples of free tools.

Desktop is the free version of Power BI, downloadable on local Windows computers. You can build interactive reports and visualizations, but the functionality is limited.

Google Data Studio is available to Google account holders and is browser-accessible. You might incur data access and processing charges when connecting to various sources.

Chartbuilder is an open-source visualization platform that requires zero coding skills. It’s a good option for web and print media professionals. You’ll need basic knowledge of data visualization concepts, though.

4. Should I opt for a BI or a visualization tool?

Evaluate BI tools against your requirements. Are they enough to meet your needs?

Here’s how effective data visualization tools differ.

  • You get specialized features like interactive charts and custom visualizations.
  • You can expect more comprehensive results as they’re more inclusive regarding data sources and types. Restrictions on importing and manipulating data can make BI platforms rigid and inflexible.
  • They’re easier to use for people who don’t have data analysis training or need to build simple presentations.
  • They may be just what you need if you’re on a budget. Many visualization tools are less expensive than BI products.
  • They work seamlessly with applications like ERP, CRM and BI systems.

5. What are some data visualization types?

Besides the usual charts, graphs and maps, graphical tools are rich in visualizations.

  • An area map shows geospatial data. Chloropleth and isopleth maps are examples of location-based visualizations.
  • A bar chart shows a side-by-side value comparison, with each bar representing a value.
  • A box-and-whisker plot, or a boxplot, compares multiple datasets from different sources, for instance, region-wise sales for the past year. It’s excellent for outlier detection.

Box

A salary comparison boxplot of NBA teams. Source

  • Gantt Charts are invaluable for project management. They show the time assigned to each task using horizontal bars that can be moved if it takes longer.
  • A heat map uses color intensity to present data trends, patterns and relationships.
  • Histograms look like bar charts but are frequency distributions. Every bar serves as a bucket for that metric’s value range.
  • A pie chart is a circular graphic shaped like a pizza with triangular portions. These represent metrics as percentages of the whole.
  • A treemap consists of tightly packed rectangles. The area of a rectangle corresponds to the size of a specific measure according to scale.

Treemap

A treemap showing electoral vote results. Source

  • A scatter plot reveals patterns and relationships between variables. Each point represents a single observation, and its position matches the values of the two variables being compared.

Know which graphic to use to scale quickly to a new visualization tool. Read our article on data visualization best practices to know more.

Next Steps

Big data visualization solutions pave the way for informed decision-making with clear insight. They give you accurate answers to queries using handy visualizations and interactive tools.

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About The Contributors

The following expert team members are responsible for creating, reviewing, and fact checking the accuracy of this content.

Ritinder
By Ritinder Kaur
Technical Content Writer
Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has eight years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds.
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Sagardeep
Technical Research By Sagardeep Roy
Senior Analyst
Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success.
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Manan
Technical Review By Manan Roy
Principal Analyst
Manan is a native of Tezpur, Assam (India), who currently lives in Kolkata, West Bengal (India). At SelectHub, he works on categories like CRM, HR, PPM, BI, and EHR. He has a Bachelor of Technology in CSE from The Gandhi Institute of Engineering and Technology, a Master of Technology from The Institute of Engineering and Management IT, and an MBA in Finance from St. Xavier's College. He's published two research papers, one in a conference and the other in a journal, during his Master of Technology.
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Hunter
Edited By Hunter Lowe
Content Editor
Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter.
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