A bespoke API can help a lot of data science projects, especially if you need to pull data from other sources and wish to speed up the process.
API design, of course, comes with its own set of obstacles and complications to tackle. So, rather than going in blind, it’s preferable to analyze the potential stumbling blocks and plan out the favorable parts ahead of time.
With that in mind, here are some key factors to consider before diving into API design.
APIs can be utilized and promoted in a variety of ways, and they can be used to achieve a variety of organizational goals.
APIs can be used commercially with a variety of monetization methods, or they can be utilized for free to boost the advertising of another product you’re selling.
As a result, identifying your API business model will be critical to making any further decisions.
The following are the most prevalent API models:
- API as a product – Without any other apps or programs, it is alone responsible for providing data and/or means of data manipulation. APIs are commonly utilized in highly specialized areas, such as finance.
- API as a product extension – APIs, which are usually a good addition to an existing application, enable more ways to meet the needs of consumers or enhance the functionality.
- API as a promotion of product – In certain circumstances, APIs are nearly entirely used to promote a company’s product or other services. Blizzard, for example, uses APIs to collect data from its games.
- Open data APIs – These APIs are frequently supplied by government or non-profit organizations and provide entirely free access to data. Such APIs, such as the Wikipedia API, is designed to provide access to useful public data or expertise.
You should also think about how you want to monetize your API. There are numerous options, but establishing what your API performs for the business is critical to your business strategy.
So, with that said, let’s go on to the 5 Rules for Designing a Great Web API, which is as follows:
It’s not the most fun component of API design, but it’s perhaps the most crucial because if documentation isn’t taken seriously from the beginning of a project, you’ll run into a slew of issues later on.
Because you rarely work in a vacuum in the realm of data science jobs, writing out the inner workings of your API is a huge step. As a result, you should expect people to need to be educated on the API’s methods and how they affect its operations.
You’re already planning to create an API for a data science project rather than using an existing solution, so you’re likely to have unique requirements and goals, as well as the skills to meet them, or at least the willingness to do so.
This puts you in a position where you can either become sidetracked by attempting to solve every problem with an altogether new system or methodology, or you can take the safer route of relying on solutions that are already widely used and hence ready for adoption.
You can get enthusiastic about the possibilities of an API in the early stages of development and hurry to put it together without considering the long-term implications.
This is significant because, in all likelihood, you will want to support this work in the long run, especially if the project it is part of has no set deadline or end date.
Any API you create to realize your data science aspirations are unlikely to be the only interface solution you utilize. Indeed, due to the sheer size of the API economy and the opportunities it provides, you’ll almost certainly be relying on several interconnected sources and solutions of this type.
As a result, it’s important to consider how your API will integrate with whatever management system you now have in place, or with any that you want to implement as the project progresses.
As you begin your road to building your own API, maybe you now have some top-level discussion points to bring to the table.
This will almost certainly be a collaborative effort, so being able to communicate clearly and successfully with your coworkers and collaborators on this type of assignment is very important to keep in mind.
Finally, when working on an API, keep in mind what your goals are because it’s all too easy to become lost if you don’t have some basic concepts and goals to guide you. You can also consider the guide to Data Science Interview Question and Answers.