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In software development, frontend refers to the presentation layer that users interact with, while backend involves the data management and processing behind the scenes. In the client–server model , the client is usually considered the frontend, handling user-facing tasks, and the server is the backend, managing data and logic.
Project Current stable version Release date License; Apache Click: 2.3.0 2011-03-27 Apache 2.0 : Apache OFBiz: 18.12.17 [11] : 2024-11-11; 3 months ago Apache 2.0
A web developer is a programmer who develops World Wide Web applications using a client–server model. The applications typically use HTML , CSS , and JavaScript in the client, and any general-purpose programming language in the server.
Django was created in the autumn of 2003, when the web programmers at the Lawrence Journal-World newspaper, Adrian Holovaty and Simon Willison, began using Python to build applications. Jacob Kaplan-Moss was hired early in Django's development shortly before Willison's internship ended. [ 15 ]
Web and mobile apps require a similar set of features on the backend, including notification service, integration with social networks, and cloud storage. [9] [10] Each of these services has its own API that must be individually incorporated into an app, a process that can be time-consuming and complicated for app developers. [11]
There are three kinds of web developer specialization: front-end developer, back-end developer, and full-stack developer. Front-end developers are responsible for behaviour and visuals that run in the user browser, back-end developers deal with the servers and full-stack developers are responsible for both.
Dynamic websites sometimes use custom web application servers, such as Glassfish, Plack and Python's "Base HTTP Server" library, although some may not consider this to be server-side scripting. When using dynamic web-based scripting techniques, developers must have a keen understanding of the logical, temporal, and physical separation between ...
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.