As we all know, nowadays, big data is getting more and more attention, and gradually become the focus of research in various industries. As the saying goes, "the good work must first make good use of its tools", big data want to do well, the use of tools must be qualified. The big data industry because of the characteristics of the huge amount of data, the traditional tools have been difficult to cope with, so we need to use more advanced modern tools, so what are the commonly used software tools for big data?

First of all, for traditional analysis and business statistics, the commonly used software tools are Excel, SPSS and SAS.

Excel is a spreadsheet software, I believe that many people are in the process of work and study, have used this software.Excel is convenient, easy to use, easy to operate, and multi-functional, provides us with a lot of function Calculation methods, so it is widely used, but it is only suitable for simple statistics, once the amount of data is too large, Excel will not be able to meet the requirements.

SPSS and SAS are both software only used for business statistics, providing us with classic statistical analysis processing, which allows us to better deal with business issues. At the same time, SPSS is simpler but also has relatively fewer features, while SAS is a little more feature-rich.

Second, for data mining, due to the important position of data mining in the big data industry, so the use of software tools to emphasize more on machine learning, the commonly used software tools is SPSS Modeler.

SPSS Modeler mainly for commercial mining to provide machine learning algorithms, and at the same time, its data preprocessing and results to assist in the analysis of the aspect It is also quite convenient, which is especially suitable for rapid mining in commercial environments, but its processing power is not very strong, and it is difficult to use once faced with too large a data size.

Third, big data visualization. In this area, the most commonly used and currently the best software than TableAU.

TableAU's main advantage is that it supports a variety of big data sources, but also has more types of visualization charts, and the operation is simple, easy to get started, very suitable for researchers to use. However, it does not provide support for machine learning algorithms, so it is not difficult to replace the data mining software tools.

Fourth, relational analysis. Relational analysis is a new analysis hotspot in the big data environment, and its most commonly used is a visual lightweight tool - Gephi.

Gephi can solve many of the needs of network analysis, powerful, and easy to learn, so it is very popular. However, it has its own limitations as it is written in Java, which results in processing performance that is not that excellent and appears to be overwhelmed when dealing with large-scale data.

The above four kinds of software, is the author for you to inventory in the big data industry commonly used software tools, these tools are more powerful, although there are a lot of limitations, but because of the big data industry division of labor is more clear, so it can also be used. I hope you can get some help from the author's article.