Considerations before designing an API for your data science project

Lots of data science projects can benefit from having a bespoke API, especially if you need to pull in data from other sources and you want to streamline this process.

Of course API design has challenges involved and complexities to overcome. So rather than going in unaware of these, it is better to weigh up the potential obstacles and also plot out the positive aspects ahead of time.

With that in mind, here is a look at the main talking points to take onboard before you dive into designing an API.

Considerations before designing an API for your data science project - featured image

Image Source: Pixabay

Document everything from day one

It is not an especially exciting aspect of API design, but it is arguably the most important one, because if documentation is not taken seriously from the start of a project, then you will face a litany of problems further down the line.

Noting down the inner workings of your API is a significant step because in the world of data science, you rarely work in a vacuum. As such, you can assume that others will need to be brought up to speed on the methods underpinning the API, and the ways in which these impact its operations.

You can of course choose a barebones approach to documentation, but it is better not just to describe and define the ‘what’ of a project, but also the ‘how’ and the ‘why’.

Tutorializing functions and processes, with clear examples that are comprehensible to new users and not just those with existing working knowledge of a project, is sensible.

This feeds into a broader consideration when designing an API for any purpose, which is that being too close to a project can blind you to its quirks and inadequacies.

Often, by writing down what you have put together with a view to explaining this to someone else, such issues will be revealed to you, and you can therefore address them proactively, rather than retrospectively when it is already in the hands of users.

Another facet of decent documentation is putting it through its paces by providing it to others for testing purposes.

Just as you would hope that your API will be rigorously scrutinized during development, so too you should rate your tutorials with the help of others to see if they are getting the desired message across in an efficient way.

Avoid the temptation to reinvent the wheel

You are already gearing up to design an API for a data science project rather than working with an existing solution in this space, so chances are that you have unique needs and aims, as well as the skills to fulfill them, or at least the desire to.

This puts you at a crossroads where you can either get sidetracked trying to solve every problem with an entirely new system or methodology, or alternatively take the more robust path of using solutions that are already in the mainstream and thus prime for adoption.

In short, there are lots of tools out there for API designers that are popular for a reason, and overlooking them in the pursuit of your goals will slow down your progress and also alienate users further down the line.

Don’t paint yourself into a corner

In the first flush of designing an API, you can get excited about the potential that it represents, and rush ahead to put it together without necessarily thinking about the future.

This is relevant because realistically you will want to support this creation in the long term, especially if the project it functions within does not have a specific deadline or end date.

In this context, you need to consider the consistency of the API experience and how it interacts with endpoints, apps and other resources connected to it.

This is not necessarily a concern if you do not anticipate implementing major changes later on, but if you have an inkling that rolling out updated versions will be required, you want to account for this initially.

Something as simple as implementing a version number can ease the transition, as changes to the system will not immediately render older examples redundant.

Consistency should apply both over time, and from moment to moment within the API ecosystem. The way you deal with data, implement naming conventions and so forth must be the same, and not shift needlessly.

This will further avoid user confusion, as well as improve compatibility and lessening the chance of calls to the API not being executed correctly.

Remember the role of API management

It is unlikely that any API you design to enact your data science dreams will be the only interface solution you use. In fact, the sheer scale of the API economy, and the opportunities this presents, means you will probably be relying on multiple interconnected sources and solutions of this kind.

As such, it pays to be attentive to how your own API will fit in with whatever management solution you have in place, or indeed with any you are intending to adopt as a project gathers momentum.

The best platforms in this space should be capable of encompassing the scope and features of your API, so long as you heed the aforementioned advice of not going too far outside the mainstream with the technologies you deploy.

Final thoughts

Hopefully, you now have some top-level talking points to bring to the table as you start your journey to designing your very own API.

This will probably be a collaborative process to boot, so being able to communicate clearly and effectively with your colleagues and partners on this type of project is another helpful aspect to remember.

As a final thought, be mindful of what your targets are when working on an API, because without some foundational principles and purposes to shore up the project, it is all too easy to get sidetracked.

If in doubt, looking at how others have used APIs to their advantage in this field will steer you in the right direction.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.