Big Data Visualization and Data Wrangling

Abstract

It’s easy to understand why self-service data analysis and visualization have become popular these past few years. It made users more productive by giving them the ability to perform their own analysis and allowing them to interactively explore and manipulate data based on their own needs without relying on traditional business intelligence developers to develop reports and dashboards, a task that can take days, weeks, or longer. Users can perform ad hoc analysis and run follow-up queries to answer their own questions. They’re also not limited by static reports and dashboards. Output from self-service data analysis can take various forms depending on the type of analysis. The output can take the form of interactive charts and dashboards, pivot tables, OLAP cubes, predictions from machine learning models, or query results returned by a SQL query.