Why cloud platforms should invest in the promise of Python

Python has emerged as the language developers want to use more than any other for building data-intensive projects. Python now powers some of the most complex applications on the cloud

According to the 2017 Stack Overflow Annual Developer Survey, Python has emerged as the language developers want to use more than any other this year for building data-intensive projects, which can range from automating robots to fueling internet of things (IoT) networks with sensor intelligence. Once an obscure scripting language, Python now powers some of the most complex applications on the cloud.

Python is on the rise in large part because it allows developers to quickly analyze and organize data, making it especially effective for streaming analytics apps built on the cloud.

Streaming analytics, or event stream processing, has become pivotal to the growth of IoT and the corresponding increase of information being collected on the cloud from sensors across industries. It’s the most effective way to keep track of events ranging from equipment failure to financial transactions, as well as to compile and analyze real-time data on the cloud or at the edge of the network.

This compatibility has spurred a greater number of streaming analytics cloud services to offer the growing Python community the tools needed to compile data and build in their preferred language. In turn, this has accelerated how quickly developers can harness real-time data for more intelligent solutions across manufacturing, finance, operations and other industries.

As the Python ecosystem continues to grow, below are some of the top reasons why cloud platforms should invest in Python development, and how cloud infrastructure and analytics services can better power innovations built with Python.

The quick rise of Python among both developers and data scientists

In the data science field, there are two main parties—developers and data scientists—that are increasingly converging. It can be seen in the code they use. Most are using either R or Python, coupled with open projects like Spark (for big data) and TensorFlow (for machine learning), for data processing and analysis. In fast-changing cloud environments, Python has seen increased interest from data scientists for its ease of use and convenience, and for its ability to effectively wrangle data sets, train machine learning models, visualize analytics, and more.

For developers, Python is a relatively easy scripting language to grasp—many developers view it as a language with clean syntax and an expansive ecosystem of libraries and tools. In addition to functional programming, Python is also a language that supports object-oriented programming, which provides a quick and consistent method for structuring code. The language’s longevity means there is a trove of documentation, which helps questions from developers as they build with it to be answered quickly.

We also can’t forget the positive feedback loop created by Python’s large and increasingly growing user base. Such a massive user base means those just wading into the world of Python have plenty of resources, including tutorials, code snippets, and its lineup of libraries for machine learning and data analytics.

This Python user base is only going to continue its growth, so it’s important for cloud platforms to start investing in building out their compatibility with the language and offering more capabilities and integrations with Python. Both developers and data scientists need to work with aggregated data across the cloud in order to fuel streaming analytics and IoT apps, so they’re going to need a language with the accessibility and utility that Python offers.

Python’s expanding potential with cloud services 

With the language growing in popularity, Python has been able to play in new fields within cloud development, such as in many data and IoT services fueled by cloud infrastructure.

For example, Python could be used to build an app on the cloud that can predict the likelihood that a vehicle engine will fail based on its temperature. After creating a Python model, which is able to easily be trained on historical failure data, a developer could connect the model into a streaming analytics cloud service to run real-time readings on incoming IoT sensor data, and then quickly configure it to alert the user if engine failure is imminent due to overheating.

Or consider a healthcare team looking to use the cloud to allow doctors to better understand patterns in patients’ vital signs. Using a combination of Python-based analytics, cloud data services and visualization tools, the development team could easily create an app which would allow the analysis of a patient’s data to be instantly visualized—in turn helping doctors to notice key health trends and spikes in a patient more quickly, without having to wait for code to be ingested and translated.

The future holds only more advancements in how the popular Python language is applied, as we all work together to make it even more accessible and applicable in the cloud. If cloud platforms prioritize Python, they can give data scientists and developers access to the new, data-intensive work processes today. Though often perceived as difficult, data science is no longer out of anyone’s reach with Python in combination with cloud.

This article is published as part of the IDG Contributor Network. Want to Join?