Big data is a broad term for collections of data so large and complex that they require specially designed hardware and software tools to process them. The data set is typically in the trillions or exabytes. These data sets are collected from a variety of sources: sensors, climate information, publicly available information such as magazines, newspapers, and articles. Other examples of big data generation include purchase transaction records, web logs, medical records, military surveillance, video and image archives, and large e-commerce.

In big data and big data analytics, there is an upsurge of interest in their impact on businesses. Big data analysis is the process of studying large amounts of data to look for patterns, correlations and other useful information that can help companies better adapt to changes and make more informed decisions

A, Hadoop

Hadoop is a software framework that enables distributed processing of large amounts of data. But Hadoop is handled in a reliable, efficient, and scalable way. Hadoopshi is reliable because it assumes that computed elements and storage will fail, so it maintains multiple copies of working data, ensuring that processing can be redistributed for nodes that fail. Hadoop is efficient because it works in parallel, speeding up processing through parallel processing. Hadoop is also scalable and can handle petabytes of data. In addition, Hadoop relies on a community server, so its cost is low and anyone can use it.

1. High reliability: Hadoop is reliable for bitwise storage and data processing.

2. High scalability: Hadoop distributes data and performs computing tasks among available clusters of computers that can be easily scaled to thousands of nodes.

3. High efficiency: Hadoop can dynamically move data between nodes and ensure the dynamic balance of each node, so the processing speed is very fast.

4. High fault tolerance: Hadoop can automatically save multiple copies of data and automatically reassign failed tasks.

Hadoop comes with a framework written in the Java language, making it ideal for running on Linux production platforms. Applications on Hadoop can also be written in other languages, such as C++.

Hadoop Big data analysis

Second, the Plotly

This data visualization tool is compatible with JavaScript, MATLAB, Python, and R languages. Plotly even helps users who don’t have the coding skills or time for dynamic visualization. The tool is often used by the new generation of data scientists because it is a business development platform and can quickly understand and analyze large amounts of data.

Data visualization for Plotly

Excel software

First of all, the higher the version, the better it is. Of course, for Excel, many people only master 5% of the Excel function, Excel function is very powerful, even can complete all the statistical analysis work! But I’ve always said that being able to play Excel as a statistical tool is not as good as being able to learn statistical software.

Data visualization in Excel

Four, Rapidminer

Rapidminer, another essential tool for big data processing, is an open source data science platform that works through a visual programming mechanism. Its capabilities include modifying, analyzing, and creating models and quickly integrating the results into business processes. Rapidminer has received a lot of attention and has become a reliable tool in the minds of leading data scientists.

Rapidminer’s Data visualization

Fifth, Smartbi

Smartbi is a powerful domestic BI report tool, compared to many need more professional mathematical ability and code ability to flexible use of big data analysis tools, Smartibi does not need too professional personnel to operate, as long as there is a need to deal with the data can be processed through Smartbi data analysis data visualization, It is convenient for users to intuitively understand valuable data.

Intelligent display of Smartbi