Using GitHub and Grafana Tools: Data Visualization (DATA VIZ) in Big Data

Abstract

We’ll talk about data visualization, why it’s important, and how to use data visualization tools called GitHub and Grafana in this article. There are several approaches to perform automatic text summarization. It can be done with supervised or unsupervised learning. And inside these categories, there are a wide variety of methods. In terms of types of summaries, there are two extractive and abstractive. The program presented here uses unsupervised learning and generates an extractive summarization. Extractive summarization is when the summary is a subset of the original text because all words in the summary are included in the original text. Data visualizations are surprisingly widespread in our daily lives, yet they frequently take the form of familiar charts and graphs. It can be utilized to find out about new things. When communication, data science, and design all come together, good data visualizations result. When done correctly, data visualizations provide significant and intuitive insights into complex datasets. This article provides a basic data visualization overview. The components regarding data visualization that should be considered throughout operational steps such as an initial argument for breakdown, communication, procedure, encoder, framework, station, and translator—which equate to six fundamentals in the framework of data visualization—are used.