Big Data Visualization Tools

Big Data Visualization Tools

Written by TSL

August, 26, 2016

A. Introduction

With the modern Internet technologies, automation and various Webscale, enterprise, cloud, and data computing in the competitive data-driven market and Internet-based economy, data at low storage cost and fast processing explode ubiquitously in both public and private sectors (Gartner, 2016). Big Data, a generic term for data in 5V’s (massive Volume, Variety in different forms, high Velocity in processing, truthful Veracity, and Value) pose major challenges in capturing and extracting meaningful information to many organizations (Davis, 2016). Recently, many organizations use data visualization and analytics to retrieve insights from their data asset for making sound decisions, increasing productivity, acquiring new customers, or gaining a competitive edge. Today, there are many visually analytical tools to perform big data analytics, particularly data visualization for presentations in the market. For example, Machlis (2011) provided twenty-two free data visualization tools for analytics; Jones (2014) listed ten leading data analytics tools in the business market, and Lurie (2014) addressed more than thirty-nine data visualization tools for cloud computing. 

This document will present ten big data visualization tools that are available in the data-driven market. It includes a short descriptive summary, typical features, colorful snapshots, highlighted benefits or advantages, and some drawbacks or disadvantages. Ten visualization and analysis tools are R Project, Google Fusion Tables, Tableau Public, VIDI, Google Chart Tools, Splunk, Qlikview, KNIME, IBM Watson Analytics, and Microsoft Power BI. All of them are free for basic applications with limited memory space (e.g., less than 500 MB per day). Some vendors of these advanced products offer a free download, training, and support services, but the others may require users to use subscription services for large enterprise projects.

B. Big Data Visualization Tools  

Many big data visualization tools have emerged in popularity over the past few years. Imanual (2015) from Predictive Analytics Today News addressed that data visualization tools make significant impacts in organizational presentations of the results. They play a crucial role in understanding data analytics outcomes. Ten of the most popular data visualization tools are addressed as shown below: 

     1. R project

          a. Summary

R Project is perhaps one of the most analytical tools in big data analytics and data visualization (Minelli, Chambers, & Dhiraj (2013). It is an open-source programming language developed for ease of use. R is the first choice in statistical analysis such as processing massive datasets in building data models with multi-purpose capability visualization. Its platform includes Linux, Mac OS X, Windows XP and later. Its skill level is 4 for users who are experienced programmers.

          b. Benefits

Users use R for the applications to find hidden patterns and unknown correlations, in-depth relationships in statistics. R can be integrated with Apache Hadoop, MapReduce or SQL Server. It has the capability in data visualization. Google uses R for statistics, data manipulation, and visualization in many services. Facebook uses R to create statistical reports to improve news feed and services. R language was also recommended to use in other fields such as healthcare, manufacturing, and marketing. R becomes popular in R communities because it is free.  

          c. Drawback

Notice that both R and Apache Spark are the popular big data analytics tools today. However, R has slower performance than Spark does because it uses memory in the hard disk drive. Users who use R are usually the experienced or highly skillful programmers because R requires the command lines.

Figure 1: R Studio GUI displays a large dataset of “extyags.nw” from one of the R library packages.

            Source: Adapted from CTU CS872 Unit 4 Individual Project, 2015.

     2. Google Fusion Tables

          a. Summary

Google Fusion Tables is a Web-based service for data management used to gather, visualize and share data tables. Data are captured and stored in multiple tables for viewing or download. It provides dataset visualization and mapping. Its platform is a browser such as Chrome, Netscape, etc. (Halevy, & Shapley, 2009).

          b. Benefits

            Data can be displayed visually in different forms such as bar charts, pie diagrams, line plots, timelines, scatter plots, or geographical maps. The data can be exported in a comma-separated values file format. It has a skill level 1 for users who have some basic spreadsheet knowledge. Google Fusion Tables is free and easy to use.

          c. Drawback

It has a limitation on customization and functionality in interacting on the massive datasets. The files that are uploaded to Google servers are limited to 250 MB for each user. Google supports data sets of 100 MB.

            Figure 2: Google Fusion Tables displays the US map data.

            Source: Adapted from http://www.computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html?page=3#fusiontables, 2015.

     3. Tableau Public

          a. Summary

Tableau Public is an analytical tool for interactive data visualization focused on business intelligence that uses a database visualization language (VizQL). Users can use Tableau Public to query data in the tables from relational and cloud databases, or Excel spreadsheets then generate many graphs combined into dashboards or shared over the Internet or networks. Tableau platform includes Windows, OS X. It skill level is rated 3 for power users (Chabot, 2014).  

          b. Benefits

Tableau is a powerful analytical tool in industries because it captures and extracts insights for data visualization presentation. Its software has won many awards for the Best Use Overall in data visualization. With the limitation of million rows, Tableau Public provides a practical playground for individual use. Tableau's visual information provides users a great means to verify hypotheses timely, explore the data, and check sanity (Jones, 2014). Tableau Public and Tableau Reader are free but its cousins such as Tableau Mobile, Tableau Desktop, etc. require subscription services.  

          c. Drawback

Tableau Public and Tableau Reader are free, but other products such as Tableau Desktop, Server, Online must be paid for an annual subscription. Tableau Public does not have the ability to create multiple dimensions in a custom group. Its limitation of creating new relationships arises in the configuration of new knowledge. At skill level 3, Tableau Public is useful for power users or programmers.

            Figure 3: Screenshot of Tableau Public.

Source: Adapted from https://public.tableau.com/s/, 2016.

     4. VIDI

          a. Summary

VIDI includes a set of the Drupal modules for creation of visual data displays. Users can display changes in data values over time in geographical maps or present static datasets in various types of charts in the Drupal system of the content management. VIDI platform is a browser with a skill level of 1 (Dataviz.org, 2016).

          b. Benefits

VIDI can capture patterns, essential themes in huge data sets very rapidly through visual means. It has many mapping options in Many Eyes at the file size of 5 MB with colorful customization. Visualization wizard makes the tool ease of use.

          c. Drawback

The VIDI tools to create these visual representations are usually too expensive and challenging for smaller news organizations and everyday citizens to use. Embed code iframe may not display properly on VIDI website.

            Figure 4: VIDI’s wizard displays a graphic on the HTML page.

            Source: Adapted from http://www.computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html?page=5#vidi, 2015.

     5. Google Chart Tools

          a. Summary

Google Chart Tools (GCT) provides a simple set of API to build customarily interactive SVG charts. GCT can visualize date at elsewhere. It supports organizational charts and geographic chart. It also provides analytics dashboard for creating an analytics page with time frame filters for charts visibility (Konforti, 2012). The platform bases on code editor and a browser. It is rated at a level skill of 2 (Machlis, 2016).

          b. Benefits

Google Chart Tools module includes a built-in library for visualization applications. Its service is rated excellently. The comprehensive API sets can take data in from a Google spreadsheet. Google Chart Tools are powerful, simple to use and free.

          c. Drawback

Google Charts do not allow users to download the google.load or google visualization code for offline using. User charts that use Google Chart Tools module will not work on IE8 (Internet Explore version 8) because IE8 does not support SVG feature. 

The API requires some coding that pushes it more programming tool.

            Figure 5: Google Chart Tools displays an analytics dashboard.

Source: Adapted from Konforti, 2016.

     6. Splunk

          a. Summary

Splunk is a data visualization tool used to search, monitor and analyze big data generated by machines or sensors through Internet browsers or Web-based interface. Splunk can capture, provide indexes, and correlates real-time data for charts, graphs, diagrams, reports, dashboards, and visual displays (Harris, 2010). Its platform includes Windows 7, 8, 10, and Windows Servers, Linux, Solaris, and Intel ICX 10.9.

          b. Benefits

Splunk provides machine-generated data accessible across an organization by providing metrics, identifying data patterns, diagnosing problems and providing intelligence for business operations. It can connect to any database sources for analysis.

          c. Drawback

Splunk builds on data indexing on the logs generated by machines and sensors, but it does not keep in view Business Intelligence objectives.

            Figure 6:  Splunk displays a search on all machine data in a real time.

            Source: Adapted from http://www.splunk.com/en_us/products/splunk-enterprise/features.html, 2016.

     7. Qlikview

          a. Summary

Qlikview has the ability of simple drag and drop techniques in self-service in the creation of data visualization without writing many SQL query commands. Qlikview can connect various databases from different vendors into Qlikview's centralized repository. It has intelligent indexing method to discover new data for patterns and trends in different data types. Qlikview provides dashboards to aid decision support systems. Its platform uses 64-bit Windows with a skill level of 2 (Qlik, 2015).  

          b. Benefits

Qlikview accepts dynamic data type formats from any source to its in-memory analytics platform. It has many channels of documentation for building big data quickly without disruption without downtime.

          c. Drawback

Schemaless or dynamic schema is not used to connect to the data source in Qlikview. It does not have a powerful graphics in comparing with Tableau’s. Qlikview is not a free product, but Qlik Sense is free.

            Figure 7: Qlikview displays a sample analysis.

            Source: Adapted from http://www.computerworld.com/article/2920545/business-intelligence/qlik-sense-free-dataviz-app-adds-public-private-sharing.html, 2015.

     8. KNIME

          a. Summary

    KNIME is an open source platform for data integration, analytics, and reporting. It

allows users to program visually for analyzing, manipulating, and modeling data in a credibly intuitive way. It uses machine learning and data mining to integrate various components through modular data pipeline concept. Users can drag connection points or drop nodes onto a canvas between activities (Abhishek, & Arvind, 2007).

          b. Benefits

KNIME is powerful analytics with vast native nodes integration in visualization with the easy-to-learn graphical interface. It is scalable and reliable within the infrastructure. It can run R, Python, text mining, chemistry data, etc. for more advanced code driven analysis. KNIME is a free and easy-to-use tool.

          c. Drawback

KNIME’s main disadvantage is the preliminary results are not available while the real pipeline was used, for example, sending and processing single rows right after they are created (Meinl, Cebron, & Gabriel, 2009).

            Figure 8: KNIME displays an analytics process and Platform GUI.

            Source: Adapted from https://www.knime.org/knime-analytics-platform, 2016.

     9. IBM Watson Analytics

          a. Summary

IBM Watson is a question answering computing for machine learning, retrieving information, presenting knowledge, and automatically reasoning. It has a capability to find the correct answer after running a hundred algorithms of proven language analysis. IBM Watson’s applications are often used in financial services, telecommunication, healthcare, and government, and game contests such as Jeopardy (Thomson, 2010). Users are not required to know statistics because IBM Watson computes all in the background. IBM Watson also provides visualization and analysis applications based on the browser with the level skill of 1.

          b. Benefits

IBM Watson is an analytics tool that has an ability to retrieve major information from all documents, provide hidden patterns, insights, and correlations across huge data sets. 80% data are unstructured in various forms such as new articles, online posts, research papers, or organizational system data (Thomson, 2010). It is a free tool.

          c. Drawback

IBM Watson has a slow response to understand the contexts of the clues. In healthcare, IBM Watson assists to identify treatment options for patients, but it has never had a chance in the process of medical diagnosis. It is still in beta stage with more upgrading versions to come. 

            Figure 9: IBM Watson Architecture in a deep question answering computing.

Source: Adapted from https://www.ibm.com/analytics/watson-analytics/us-en/, 2016.

Figure 10: IBM Watson shows high diamond prices below.

Source: Adapted from https://www.ibm.com/analytics/watson-analytics/us-en/, 2016.

     10. Microsoft Power BI (Business Intelligence)

          a. Summary

Microsoft Power BI is a business analytics tool for analyzing data and providing insightful information similar to Excel’s Power Query. It can be used for monitoring the business and sharing timely answers on the convenient dashboards. With drag-and-drop features, Power BI use natural language to report data in a visual format or find good responses to difficult questions (Power BI, 2016).

          b. Benefits

Microsoft Power BI platform provides better price-performance ratio for data visualization (DV) with the most robust set of BI and DV modules such as SQL, Sharepoint, Server, Analytical Reporting and Integration Services, Excel 2010 with PowerPivot add-in.

          c. Drawback

Power BI’s implementation is about average with good scalability and good data integration, but its dashboard support is weak.

            Figure 11: Microsoft Power BI displays airlines’ departure and arrival delays.

            Source: Adapted from http://www.computerworld.com/article/3088958/data-analytics/free-data-visualization-with-microsoft-power-bi-your-step-by-step-guide-with-video.html, 2016.

E. Summary

This document presented a descriptive evaluation report of ten advanced big data visualization tools that were used for answering most of the difficult questions, extracting meaningful information, and revealing insights, hidden patterns or correlations across data in various sources in the data-driven market. They included R Project, Google Fusion Tables, Tableau Public, VIDI, Google Chart Tools, Splunk, Qlikview, KNIME, IBM Watson Analytics, and Microsoft Power BI. For each analytical tool, a descriptive summary, typical features, graphical snapshots of the images, benefits, and drawback were described with highlighted information from a variety of the scholarly resources and credible websites.  

In summary, this document described ten advanced data visualization tools to present big data visualization results and outcomes from the robust analyses in today’s data explosion market.

REFERENCES

Abhishek, T., & Arvind, S. (2007). Workflow based framework for life science informatics. Computational Biology and Chemistry.31 (5-6): 305–319.

Chabot, C. (2014). How to get a 20 million dollar pre-money for series a:tableau software. Retrieved August 22, 2016 from

http://www.sramanamitra.com/2010/03/05/how-to-get-a-20-million-pre-money-valuation-for-series-a-tableau-software-ceo-christian-chabot-part-3/

Dataviz.org, (2016). How it works. Retrieved August 23, 2016 from http://www.dataviz.org/how-it-works

Davis, J. (2016). 2016 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms. Retrieved August 21, 2016 from

http://www.informationweek.com/big-data/software-platforms/gartner-bi-magic-quadrant-inflection-point-has-arrived/d/d-id/1324233

Gartner Group (2016). Gartner BI magic quadrant: inflection point has arrived. Retrieved June 4, 2015 from

http://info.birst.com/AR-Gartner2016CriticalCapabilities_LP.html

Halevy, A., & Shapley, R. (2009). Google fusion tables. Retrieved August 22/2016 from

https://research.googleblog.com/2009/06/google-fusion-tables.html.

Harris, D. (2010). How splunk is riding it search toward an ipo. Retrieved August 23, 2016 from https://gigaom.com/2010/12/17/how-splunk-is-riding-it-search-toward-an-ipo/

Imanuel (2015). 50 big data platforms and big data analytics software. Predictive Analytics Today. Retrieved on November 16, 2015 from http://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/

Jones, A. (2014). Top 10 data analysis tools for business. Retrieved August 21, 2016 from

http://www.kdnuggets.com/2014/06/top-10-data-analysis-tools-business.html

Konforti, R. (2012). Google chart tools. Retrieved August 2016 from

https://www.drupal.org/project/google_chart_tools.

Lurie, A. (2014). 39 Data Visualization Tools for Big Data | ProfitBricks Blog. Retrieved August 21, 2016 from https://blog.profitbricks.com/39-data-visualization-tools-for-big-data/

Machlis, S. (2011). 22 free tools for data visualization and analysis. ComputerWorld. Retrieved August 8, 2016 from http://www.computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html

Meinl, T., Cebron, N., & Gabriel, T. (2009). The konstanz information miner 2.0. Reterived August 23.2016 from https://kops.uni-konstanz.de/bitstream/handle/123456789/5762/main.pdf;sequence=1

Microsoft Power BI (2016). Bring your data to life. Retrieved August 23, 2016 from https://powerbi.microsoft.com/en-us/?WT.srch=1&WT.mc_id=AID529580_SEM_uDaUULKn&utm_source=Google&utm_medium=CPC&utm_term=microsoft%20power%20bi&utm_campaign=Power_BI&gclid=Cj0KEQjw6O-9BRDjhYXH2bOb8Z4BEiQAWRduk_e-USXb3hqcbKLjs43WZuqXyMhACPamjd1J7Nwju6UaAkL_8P8HAQ

Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons.

Qlik (2015). Make stunning data discoveries . Retrieved November 2, 2015, from http://www.qlik.com/products/qlik-sense

Thomson, C. (2010). What is i.b.m.’s watson? Retrieved August 23, 2016 from

http://www.nytimes.com/2010/06/20/magazine/20Computer-t.html?_r=0