Creating a digital product may require integrating it with third-party companies and products (for example PayPal or Dotpay) as well as a variety of different systems, technologies, and existing databases.
Is Python easy to integrate? How to leverage the power of Python when it comes to the integration process? Read a guide for entrepreneurs and product owners.
Python is an interpreted programming language that emphasizes code readability. It has also been called a “glue language” because of how easy it is to integrate Python with other components. This makes Python an obvious fit for many of the newest trends in software development, which favor modular programming that “frankensteins” products together from the best available programming languages, frameworks, and external services.
Python is a popular choice for app development among enterprises that may already use a variety of different systems, technologies, and existing databases, and among startups who favor Python’s versatility in allowing them to make quick use of third-party features and rapidly develop an application from concept to roll-out.
Python provides native support for C and has extremely robust solutions for integrating with other languages like C++, Java, Rust, and Go. Perhaps the best feature of Python when it comes to integrating with existing technologies is that “glue language” factor. Rather than rebuilding existing technologies and systems from scratch, Python app development can simply use Python’s REST API or FFI (Foreign Language Interface) to bind them together.
One advantage of this ability to integrate existing technologies is that an enterprise can have two teams working in parallel on the same project. Both teams are able to work in their own way, using the language and technology that they’re most comfortable with, and they can then integrate the effects of their work using Python.
At the same time, if a business has an in-house application that was already developed in Python and wants to bring in other languages like C++ or Rust for modules where those languages are better suited, Python can effectively join them all together, without the necessity of developing a large codebase.
This can save businesses a lot of time and money as they are able to integrate existing systems instead of rewriting them.
Usually, making use of an API (Application Programming Interface) provided by a third party is just a matter of a few fairly simple lines of code in Python using convenient tools like REST API or JSON. What’s more, Python’s large base of free tooling for using APIs has been constantly improved by a passionate base of programmers for the more than 20 years that Python has been on the market, meaning that it is highly capable of handling a lot of different third-party products without difficulty.
In many cases, Python app development may require integration with third-party companies or products in order to access their functionality. An example would be Paypal or Dotpay, which add the ability for users to make in-app purchases quickly, easily, and securely. Another would be APIs that can find the location of nearby restaurants in-app or connect with the user’s friend list from another app or service.
For an app developer to add in this kind of storefront coding from scratch would be costly and time-consuming, not to mention potentially less secure or accessible than established services like Paypal. It’s also worth it to notice the legal restrictions and risks that come with such features.
Fortunately, these services generally offer APIs that can be integrated into an app quickly and easily using Python.
The advantages of this for business are obvious, as incorporating an existing service via API takes a lot less time, costs a lot less money, and carries a lot less risk than developing such solutions in-house. And with Python app development, it’s relatively simple to incorporate these features from third-party providers.
Most businesses aren’t starting out from scratch. They have customer lists, invoices from previous years, and all sorts of other information, usually stored in some sort of existing database so that it can, theoretically, be easily accessed.
Integrating an existing database often means re-entering all of the data from it by hand. This, obviously, takes hours, and is prone to errors. Python offers an easy way around that, thanks to a wide range of robust and stable grab-and-use libraries that can integrate any database engine out there.
Whether a business is using a relational database tool like MySQL, Postgres or Sqlite, which present data in tables and rows such as might be seen in Excel, or a non-relational database like MongoDB, which stores data as a series of JSON files, Python has simple, accessible solutions for integrating it.
This allows businesses and developers to quickly and easily access, modify, store, or delete existing data as needed. Python can also help to pull metrics from existing databases, making the Python interface much easier (and safer) than working with a raw database.
Libraries like SQLAlchemy or DjangoORM also take some of the peril out of modifying existing databases. Working directly in the raw database and making changes manually can potentially result in small errors that can add up to the loss of large amounts of data. These Python-friendly libraries help make keeping databases consistent much simpler and less risky.
If a company is just starting out, or needs to start tracking new metrics that don’t relate to an existing database, the news is even better. Creating a new database with Python is even easier than integrating an existing one.
The default Python database table structure makes speeding up the development process simple and straightforward, while tools like Django Framework with DjangoORM automatically take care of all the interactions with the database, effectively cutting down on workload and speeding up the development pipeline.
A multitasking tool like Python is great for most applications, but every now and then a more focused approach is better. This is where frameworks like Django that adapt universal languages to specific tasks come in handy. Fortunately, these can easily be incorporated into Python in an almost plug-and-play capacity.
Python’s nearly effortless integration with both new and existing databases obviously helps businesses and developers to speed up the development pipeline and reduce overhead, but it has other advantages, too. Using Python’s robust collection of existing libraries helps to make apps that are virtually immune to common database attacks. This leads to better security and increased trust among end-users.
Python also boasts a massive and passionate community of coders and enthusiasts, making it easy to find programmers, and ensuring a stream of new tools and libraries, as well as constant refinement to ensure that Python solutions are as mature as possible. And that benefits everyone who uses it.