The usefulness of JSON lies in the fact that it's versatile and relatively concise. What's more, it's faster than other similar file formats, while it's already widely used for web-related applications, making it easy to find mature programming libraries for it. Moreover, JSON is very intuitive, and many text editors have built-in functionality for viewing such files in an easy-to-read way. Furthermore, it's easy to create and edit JSON files yourself using a text editor, while programmatically, it's a walk in the park.
JSON’s compatibility with NoSQL databases is one of its fortes. Such systems include databases like MongoDB, which are quite popular in data science. Most new databases are also compatible with JSON as it's become a kind of standard. Additionally, JSON and the dictionary data structure go hand-in-hand, something vital in data science work. So, if you want to load some data from a JSON file, you can store it in a dictionary, while if you have a dataset (any dataset), you can code it as a dictionary (each variable being a key) and store it as a JSON file.
The JSON.jl library in Julia is one worth knowing about, especially if you want to use this programming language in your data science work. This fairly simple package enables you to parse and create JSON files, using the primitive Dict structure. A convenient library to know, even if it's still in version 0.21.x. JSON.jl makes use of the FileIO package on the back-end and its most useful functions are parse(), parsefile(), and print(). Note that the latter works different data structures, not just dictionaries.
The JSON file format is closely linked to APIs too. The latter are particularly useful in various data-related applications and are instrumental in certain data products developed by data scientists. Also, many APIs are essential for acquiring data, so knowing about them goes without saying. APIs are ideal for proof-of-concept projects, too, as they don't require too much work to get one up-and-running. As a result, they are a versatile tool for all sorts of projects, particularly those with a web presence.
The API Success book describes this technology in sufficient depth, without getting too technical. Besides, if you understand APIs' usefulness and how they fit into the bigger picture, it's not too hard to learn the technical aspects too, through a tutorial, for example. Note that you can get a 20% discount on this and any other book available at the publisher's website using the coupon code DSML. Using this code will also help me out, so you can see it as a way to support this blog. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.