Enterprise Engineering: Python
With the software industry and development booming in 2021, utilizing a powerful data tool that can support backend infrastructure, middleware, and total development has never been more important. This week’s Enterprise Engineering series digs into Python and the features, trends, and capabilities that can impact your business.
Why do Python’s Frameworks and Libraries Stand Out?
One benefit of using Python frameworks is they are extensive, allowing you to find practically any function needed for your task. Developers can use “plug and play” code without needing to depend on external libraries or writing functions from scratch [5]. Python is also one of the only programming languages with a library of modules that allow cybersecurity professionals to use penetration testing tools at any time [5].
ETL Development
When it comes to Extract-Transform-Load (ETL) development, Python offers multiple frameworks to fit your needs. One lightweight ETL framework known as Bonobo can be utilized for Python 3.5+ and provides the tools for creating data transformation pipelines, using plain Python primitives, and executing them in parallel. This lessens runtime and avoids problematic bottlenecks [6].
The framework Pygrametl is compatible with CPython and Jython, and is great for pipelines built with multiple languages. According to the developer of Pygrametl, Christian Thomsen, the framework is heavily used in the production of industries like healthcare, finance, and transportation [6]. Lastly, a lightweight ETL framework, called Mara, focuses on transparency and complexity reduction; sitting halfway between plain scripts and Apache Airflow, but requires access to PostgreSQL as a data processing engine.
Middleware Software Integrations
Middleware is crucial for a reliable framework, and Python provides integration solutions suitable for both front and backend services. Python’s Django includes Authentication Middleware that associates users with requests using sessions [4]. Another Python product for middleware is Flask which tends to have higher compatibility with modern technologies as well as high scalability for simple applications.
Machine Learning
As machine learning becomes more popular in 2021, Python’s efficient ML packages and tools for visualizing results is a great asset. A library known as Scikit-learn creates learning models and has countless tools for predictive modeling. Scikit-learn also allows developers to build supervised and unsupervised ML models as well as cross-validate the accuracy of those models [6]. TensorFlow is another adaptable and efficient framework that provides a direct path to production and is effective in data mining and analysis.
TensorFlow allows developers to train and deploy your model with ease whether it is on servers, devices, or the web. The extended version, TFX, can even provide full production of machine learning pipeline [7]. Another framework well equipped for machine learning is NumPy. It has multidimensional arrays that are 50 times more robust compared to Python lists, making it a top favorite for data scientists. NumPy is also time-efficient since it provides precompiled functions for numerical routines that are rather difficult to solve manually [6].
2021 Trends
AI projects and the emergence of all things telehealth due to the pandemic will greatly depend on the frameworks of Python. The need for readable, scalable, and extensible domains within business applications has never been more important; and Python's vast products and capabilities are already in use across platforms like Instagram, Google, Spotify, and Dropbox [1].
Limitations
One of the reasons Python is so popular is for its flexibility, however, this results in high memory consumption making it less realistic to complete memory-intensive tasks [5]. Python is an interpreted script language with line-by-line execution of code; this may lead to speed limitations and slower progress and lacks multiprocessor support usually used when writing an application. However, other programming languages like Ruby, Perl, and JavaScript have similar executions and results.
Overall, no programming language is perfect or generally used in a standalone fashion. Weigh out your options and determine what enterprise engineering fits your needs and check out previous backend enterprise assessments from this article series.
Whether you’re working with Python or another technology with a limited talent pool for experts in the field, STAND 8 can provide the end-to-end solutions and resources to get your project off the ground and on track for delivery. Reach out today!
By: Haley Graven and Jessica Delaney
Other Articles in this Series
Backend Enterprise: Java
https://www.dhirubhai.net/pulse/backend-enterprise-applications-java-jessica-delaney/? trackingId=m6AOOyS4FvcNEBTo5Wm3QA%3D%3D
Backend enterprise: Ruby on Rails
https://www.dhirubhai.net/pulse/ruby-rails-2020-jessica-delaney/
Resources:
- Actors, Agile (2021). “Top Python Trends For 2021.” https://medium.com/agileactors/top-python-trends-for-2021-897d34c0d22
- Chekalin, Dmitry (2020). “Top Programming Languages to Shape Software Development in 2021.” https://www.codica.com/blog/top-programming-languages-trends/
- Council, EC (2021). “Why Python is Important for Security Professionals.” https://blog.eccouncil.org/why-python-is-important-for-security-professionals/
- Django (2021). “Middleware” https://django.readthedocs.io/en/stable/topics/http/middleware.html
- Korsin, Julie. “The 16 Most Important Pros and Cons of Using Python for Web Development.” https://djangostars.com/blog/python-web-development/
- Smallcombe, Mark (2020). “The Top 4 ETL Python Frameworks.” https://www.xplenty.com/blog/top-etl-python-frameworks/
- TensorFlow. “Robust ML Production Anywhere.” https://www.tensorflow.org/about