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Introduction to Big Data Visualization

For analyzing big data efficiently, the most important thing is to choose perfect visualization tools. A perfect visualization tool will generate an efficient visual diagram which will lead to a correct decision. Insufficient visualization will lead to a loss for the organization.

On Facebook, 4 petabytes of data are uploaded per day that contains different information like video, images, or textual information. Without visualizing those, it's hard to understand patterns and other relevant information.

  • Facebook uses "HiPlot" to analyze and visualize big data.
  • Another large organization IBM uses "Big SQL" integrated with other visualization tools like zeppelin notebooks, Data Science, Tableau, and Cognos. Amazon uses AMZ Base, Amylaze, SellerApp, etc.

The selection of efficient big-data visualization tools will help change complex and extensive volume data into simple and human-readable visual diagrams. This visual diagram helps analysts predict more accurately that it will lead to business improvement.

Visualization provides a quick, easy way to convey concepts and summarize and present large data in easy-to-understand and straightforward displays.Click to explore about our, Advanced Data Visualization Techniques

What are the Big Data Visualization challenges?

Big Data is a large volume, complex dataset. So, such data can not visualize with the traditional method as the traditional data visualization method has many limitations.

  • Perceptual Scalability: Human eyes cannot extract all relevant information from a large volume of data. Even sometimes desktop screen has its limitations if the dataset is large. Too many visualizations are not always possible to fit on a single screen.
  • Real-time Scalability: It is always expected that all information should be real-time information, but it is hardly possible as processing the dataset needs time.
  • Interactive scalability: Interactive data visualization help to understand what is inside the datasets, but as big data volume increases exponentially, visualizing the datasets take a long time. But the challenge is sometimes, and the system may freeze or crash while trying to visualize the datasets.

What is meant by Big Data?

Big data contain a large volume of data with great variety, and this dataset increases its velocity exponentially. Big data could be structured, unstructured, or semi-structured. Managing big data is a very tedious task. With time big data popularity increases as we show interest in extracting information from that data. The volume of big data increases exponentially with time. Big data cannot be stored in our traditional database.

As per information, 720,000 hours of data have been uploaded on youtube. According to a survey, most of the data is unexplored. Every organization speeds up its analysis of big data to find new opportunities for the development of the company. It will reduce costs and will increase company profit.

A new, data-oriented mind-set is permeating the business world. Click to explore about our, Data Visualization Is The Future

Why Big Data Visualization Tools important in businesses?

Big data visualization tools give a chance to drive depth in the vast data. As a result, one can find new patterns in the data or any errors. And from that newly generated pattern, more decisions, as well as actions, can be taken.

  • Better Data Analysis: Visualizing tools generated reports help the organization management committee decide what will happen in advance. Visualization tools generate information that is very important to understanding the current growth of the organization. Better data visualization also helps in better decision-making.
  • Decision making: The human brain responds very fast to visual diagrams instead of text data. Visualization tools generate diagrams that help make fast decisions and business growth simultaneously.
  • Help in sensing complex data: Big data is stored in a very unstructured way. As per the definition, big data contain various data like video, audio, images, and textual data. Such combined dataset reading is hard for humans as that dataset is in a complex format. With the help of data visualization tools, meaningful, relevant information in simple pattern extraction is possible from such datasets. Sometimes new relevant patterns can be explored even if there are any errors in the datasets.
  • Time-saving: Once data visualization tools read the dataset, they will plot diagrams. So, it saves time as well as money. And big data visualization is not possible without any data visualization tools.
  • Error detection and correction: Data visualization tools are also helpful in finding out errors in the dataset. If a dataset contains any error, it is possible to take some actions to solve that. And it is possible to arrange the dataset as per requirement.
Communicate the message and understand patterns easily and frequently.Click to explore about our, Data Visualization Designs

Purpose of Big-Data Visualization tools

Big-data visualization tools' purpose is to read complex unstructured data with the help of visual diagrams to benefit our business. One relevant example is the covid 19 affected report. That is a large dataset. Only data scientists can read and find out the pattern and predict the percentage of affected patients.

Big Data Visualization Tools

Nowadays, there are many data visualization tools. Some of them are:

  • Google Chart
  • Tableau
  • Microsoft Power BI
  • D3 (Data-Driven Documents)

Google Chart

Google Chart is one of the most accessible tools for visualization. With the help of google charts, you can analyze small datasets to complex unstructured datasets.
We can implement simple charts as well as complex tree diagrams. Google Chart is available cross-platform as well.

Tableau

The tableau desktop is a very easy-to-use big data visualization tool. Two more versions are available of Tableau. One is "Tableau Server," and the other is cloud-based "Tableau Online." Here we can perform visualization operations by applying drag and drop methods for creating visual diagrams. In Tableau, we can create dashboards very efficiently.

Microsoft Power BI

This tool is mainly used for business analysis. Microsoft Power BI can be run from desktops, smartphones, and even tablets. This tool also provides analysis results very quickly.

D3

D3 is one of the best data visualization tools. D3.js is an open-source visualization tool.ig

Datawrapper

Datawrapper is a simple tool. Even non-technical persons can use the Datawrapper tool. Data representation in a table format or responsive graphs like a bar chart, line chart, or map draws quickly in the Datawrapper.

Databox

Databox is another visualization tool. It is an open-source tool. The whole data set can store in one location in the Databox tool. Then discover the insight data and perform visualization operations. In dashboard can view or match data from different datasets.

Many more data visualization tools are available per requirements and based on datasets. Visualization tools are chosen.

A method of translating raw data into visual forms like charts, graphs, and maps. Click to explore about our, Best Practices for Data Visualization

Approach to Visualize Big Data

Big data is unstructured, and such data can be very easily stored on a NoSql database like MongoDB or relevant information needed to extract from the data and store on a SQL database. Then from that dataset, with the help of data visualization tools, some charts like bar charts, pie charts, etc., need to plot. Then from those visual chart analyses can be performed.

  • NoSql database is mainly used for storing unstructured data like Big Data.
  • Choose appropriate data visualization tools
  • Use different algorithms as per requirement.
  • Visualize the dataset.

Use Cases of Big Data Visualization Tools

  • Sports Analysis: Based on previous datasets with the help of visualization tools, a winning percentage prediction is possible. Graph plotting for both teams or players is possible, and analysis can be performed.
  • Fraud Detection: Fraud detection is a popular use case of big data. With the help of visualization tools after analyzing data, a message can be generated to others, and they will be careful about such fraud incidents.
  • Price Optimization: In any business product, price set is a significant issue with visualizing tools and all the components used; price can be analyzed and finally compared with market price, and then a relevant price can be set.
  • Security Intelligence: Visualizing criminals' records can predict how much threat they are to society. Each country has its security intelligence, and its task is to visualize information and inform others about a security threat.
Java vs Kotlin
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Conclusion

This is the era of data, and nowadays, nothing is more expensive than data. All companies and organizations are now involved in big data. For our growth, we also need to learn about big data and its visualization tools to understand datasets and invest our valuable time in such precious fields. All companies and organizations are searching for efficient professionals in this field. Finally, Big Data is an excellent opportunity for future growth.

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