One of the most important utilities of big data is the ability to visualize them to make sense of the data that has been collected.
They range from easy-to-use and very basic tools for the complete beginner right through to tools for the more savvy user that offers more control and customization and the ability to scale the tool up as your business grows.
Using these tools, an organization can maximize its data, analyze it, and make the right decisions for success in the current world atmosphere.
Popular Big Data Visualization Tools: A Detailed Comparison
1. Tableau
Tableau allows users to create interactive and shareable, dashboards from complex data. It is able to work with big data platforms.
Advantage:-
- Easy to navigate with some features being designed to be moved around by the user.
- Integration with multiple data sources, Big Data Platform like Hadoop type Data & other Cloud Service Providers like AWS.
- Big data analysis and appli-cation of artificial intelligence.
- Lot of templates available for optimizing the visualization process.
Disadvantages:
- Own licensing cost remains high for the use within enterprise environment.
- The advanced features of this software tend to have a somewhat steep learning curve.
Use Case: A retail company employs use of Tableau to analyse revenue patterns so as to increase value density in separate geographical locations.
You may also read: What are common Visual Big Data Analysis Technique
2. Microsoft Power BI
Power BI gives interactive visualizations and business intelligence features to generate real-time insights and collaborate.
Advantages:
- Compatibility with Microsoft products such as Excel and Azure.
- Relatively cheap with a basic option that is free of charge for use by users with less than 125 employees.
- Up-to-date data in dashboards based on the real streaming of the data.
- Using data insight supported by artificial intelligence or asking questions in natural languages.
Disadvantages:
- Not as much customization as tools such as Tableau.
- These may be used where you have very large datasets that may sometimes cause performance bottlenecks.
Use Case: Power BI is used by a financial institution to track key performance indicators, including revenue growth, the costs, and customers.
3. Google Data Studio
Being free, Google Data Studio is a web-based tool that allows you to create your own customizable and shareable dashboards mostly on Google services, like Google Analytics and Google BigQuery.
Advantages:
- Online and free for use provided that Google accounts are used for accessing them.
- Adwords, Infinite Analytics & BigQuery, Docs and many others Google services.
- Responsive and intuitive designs for templates and sections of a website.
- Real-time collaborative features and motion.
Disadvantages:
- Lack of Non-Google data sources.
- Personal web visualizations versus professional software tools.
Use Case: A campaign report on the effectiveness of a digital marketing agency, the tool employed in the process is Google Data Studio.
4. QlikView and Qlik Sense
QlikView / Qlik Sense are data visualization tools to discover the relationships in the data using associative indexing.
Advantages:
- This offers a basis for associative data indexing through which new and hitherto unknown connections between the data can be identified.
- Upgradation of analytical tools with incorporation of concepts of artificial intelligence and Machine learning.
- To support comprehensive and high-capacity big data applications.
- Capabilities for presenting the insights through storytelling.
Disadvantages:
- Using the stock trading system requires some time to master due to the multiple icons on the screen.
- It’s more costly for enterprises to get a solution from the service.
Use Case: A healthcare provider employs Qlik Sense for identify the ability to interpret patient information so that the resources can be well utilized.
5. D3.js
D3.js is a JavaScript library for manipulating documents based on data with the intention of simplifying interactivity.
Advantages:
- Customization at a high level for creating unique visualizations.
- Extremely open source, super flexible.
- It integrates with HTML, SVG, and CSS for web-based applications.
Disadvantages:
- It requires coding knowledge, thus being less user-friendly to the non-developer.
- Time-consuming to create visualizations from scratch.
Use Case: For example, D3.js is used by a startup to create an interactive visualization of what user behavior looks like on their platform.
6. Apache Superset
Apache Superset is an open source data visualisation and exploration tool that is lightweight, fast, and highly scalable.
Advantages:
- Good for large datasets, fast, lightweight.
- It connects to different data sources, such as SQL based databases.
- Extremely customizable open source project.
Disadvantages:
- Very little documentation, and little community behind it compared to other tools.
- It is technically complicated to configure and maintain.
Use Case: Apache Superset is used by a tech company to visualize how performance metrics aggregate over its cloud infrastructure.
7. Sisense
Sisense is a business intelligence platform harnessing data from various sources they put on a single dashboard for advanced analytics.
Advantages:
- It combines data from various sources into a single dash board.
- AI and advanced analytics driven insights.
- Large datasets, enterprise environments.
- Custom branding white labeling options.
Disadvantages:
- Small businesses, however, found it to be too high.
- It is a tool that requires training to use most of the advanced features.
Use Case: Sisense is used by a manufacturing company to watch for improvements in production efficiency and supply chain metrics.
8. Kibana
Kibana is an open-source visualization tool for data stored in Elasticsearch, providing real-time monitoring, and exploration.
Advantages:
- Data monitoring and alerting in real-time.
- Open source but also highly customizable.
- Includes pie charts, heatmaps, and timelines as examples of visualizations.
Disadvantages:
- There is limited data stored in Elasticsearch.
- It is an advanced configuration, which needs technical expertise.
Use Case: Monitoring for potential threats is performed by a cybersecurity firm with Kibana, which runs network logs.
9. Matplotlib and Seaborn
Both are Python libraries for creating static, animated, and interactive visualizations using Matplotlib and Seaborn. High-level abstractions are provided by Seaborn, which is built on Matplotlib.
Advantages:
- Full customization options.
- It’s ideal for coding-based analysis, visualization, and reporting.
- Heatmaps and pair plots are high-level abstractions Seaborn brings to us.
Disadvantages:
- It demands a knowledge of Python programming.
- Unsuitable for real-time data analysis and a large number of dashboards.
Use Case: In order to create detailed visualizations for a research paper, a data scientist uses Matplotlib.