3 Ways of Using Python in Enterprise Projects
Python is known a dynamic object-oriented programming language and it has the ability to integrate many other languages and tools. Many developers like this language because it is quick to write, speedy to debug and even faster than many other languages. Each new version of Python is getting more optimized, however as any other technologies, it has limitations too. Applications made with this language can be found at a very large scale in big companies like Google, Instagram, YouTube and PayPal.
There are many ways of using Python in enterprise projects and in this article, we will touch three of them:
1) creation of complex web-solutions with Python
2) working with Big Data and Analytics
Creation of complex web-solutions with Python
According to the several resources, Python is considered as the most important tool for backend development in enterprise projects. Besides this, the language is very helpful for building server-side web application.
For maintaining security record and high-performance level of commercial and open-source projects, Python enables developers with powerful tools. It is clean and easy to read hence debugging, adding new feature and maintain old what makes this language a good choice for using in enterprise projects.
There are many python frameworks and one of them is Django which is considered most loved and secure for building web solutions.
DevOps is taking over the industry and compatibility of Python with many operating systems makes it more suitable for this. It is popular programming language among the DevOps community due to its ease of use, less space consumption, compatibility with big data and ability to handle complex tasks.
Ansible and other popular DevOps tools are written in Python and can be controlled using this language.
Working with Big Data and Analytics
Python goes hand to hand with data sciences and it has different libraries like Pandas to make work with a big volume of data easy and simple. Python can be applied to achieve accurate data analysis. Among the libraries which are used in BA analytics are:
• NumPy: use for scientific computing
• SciPy: it is a mathematical algorithm
• Pandas: used for data manipulation
• IPython: use for creating and cleaning reports and statistics
• Matplotlib: use for visual representation of data using graphs
The storage and retrieval of operational data is done in ERP (enterprise resource planning) system using relational database and with the bunch of scientific packages libraries. Python is a good fit for this.
To conclude, Python can be marked as a good choice for enterprise projects because of its modernity, possibility to solve complex tasks and support of the sizable technical community.