JSON vs CSV: What's the Difference?

There are two extremely popular file formats that are used in numerous ways – JSON and CSV. They’re widely used in data transmission, storage, and analysis. You’ll also sometimes find people arguing about which file format is better for which use case.

While both are popular formats, in JSON vs CSV, one of them is better in some areas and worse in others. Picking the correct format will save you a lot of headaches along the way, regardless of your use case.

What Is JSON (JavaScript Object Notation)?

JavaScript Object Notation, shortened to JSON, is a file format that was originally intended for transmitting data between web servers and clients. It has since expanded way out of the somewhat narrow initial use case and now you can find a JSON file in most applications.

Despite what the name might suggest, JavaScript Object Notation doesn’t require JavaScript to be used. JSON can be seen as a slightly more complicated but useful text file with a different data structure.

One of the main reasons for JSON’s popularity is that it’s easily readable and understandable for both humans and machines. So, it’s widely used for various types of data storage, interaction with APIs, and data extraction.

Basic Structure of JSON

JSON uses a tree-based data structure that has several elements to it: objects, arrays, and key-value pairs. Additionally, it’s a nested structure with certain values being stored within objects or arrays.

There are possible arrangements of JSON data where there are no arrays. Technically, there can be JSON data without any of the elements. However, these are exceedingly rare as a lot of the value of hierarchical data structures are lost.

{ // <- indicates start of object
    “name” : “John” // <- key-value pair
    “usernames” : [“JohnSmith”, “SmithJohn”, “MrJohn”] // <- array

JSON data can also start as an array and hold objects:

[
    {“name” : “John”, “username” : “JohnSmith”}
    {“name” : “Ted”, “username” : “Tedder”}
]

There are many other ways to structure JSON data. You can just list a string, a single array, or even a boolean value (e.g., “True”). Most commonly, however, you’ll encounter JSON data with numerous objects (and objects-within-objects) using a nested structure.

Key Features of JSON

As mentioned previously, JSON has numerous benefits and advantages when used for data storage, analysis, or programming:

1. Human and machine readability

A simple, but effective structure makes JSON easy to read. It’s also great even when hierarchical data structures are used – something that may normally be more difficult to parse.

2. Decent at handling complex data

A nested structure makes JSON quite handy when you need to store data with numerous qualities and values.

3. Lightweight

JSON is often the go-to for APIs and various other ways of interacting with servers as it’s easy to parse while also being lightweight.

Numerous distinct advantages have made JSON the go-to data storage format for specific types of information. It’s not as great, however, when it comes to large volumes of tabular data.

What Is CSV?

CSV or Comma-Separated Values is a popular format for storing tabular data. Most people are familiar with it through Google Sheets, Microsoft Excel, and many other similar programs. While these have their own unique file extensions, the CSV format remains foundational.

Basic Structure of CSV

The structure of the CSV (comma-separated value) format is rather simple – there are rows and columns with values being separated, as the name suggests, by commas. While it may be structured in any way, the first row is often used as the header row to store names for columns.

Each line (after the first) stores a row of data with values being separated by commas. Loading the tabular data of a CSV file into some software will usually turn the data into columns.

Here’s an example of a CSV file wherein it’s used to list data about books:

There are a few distinct advantages to using commas instead of a new column for each data point. First, if you have information that may differ between data points (say, “cities lived in” for authors), you may end up with empty cells, complicating analysis.

Additionally, most advanced software can easily handle delimiters and turn data into columns if necessary. Finally, the CSV file format is intended to be lightweight – delimiters are more space-efficient than columns.

Key Features of CSV

Comma-separated values lend themselves to a few distinct advantages:

1. Efficient for large datasets

As mentioned previously, delimiters are space efficient, so large datasets are significantly easier to handle with comma-separated values.

2. Widespread support

CSV files are supported by numerous programming languages, data analysis tools, and plenty of other software. It’s one of the most widely used data storage and entry formats.

3. Relatively easy to understand

Smaller files without complex data structures are easy to understand, even for humans. Things get a little bit more complicated with large datasets, but dedicated software is usually used to make use of the file.

There are many other good reasons to use CSV for various data types. Since it’s so familiar, many professionals know their way around tabular data, at least to some degree, so it lessens the learning curve.

Key Differences Between JSON and CSV

As you may have noticed, the two formats are quite different. While they can store the same data types, both file formats do it in a different fashion. Therefore, one of them will be better at some tasks over the other.

• Data Structure

JSON is better for complicated data with numerous qualities, arrays, and objects. CSV is better for flat data with fewer qualities.

• Readability

JSON is human-readable throughout and doesn’t get much more complicated with size. CSV is somewhat readable, however, gets difficult to parse when used for large volumes of data.

• Size and efficiency

JSON, all things being equal, will be less space efficient as the structure and labels lead to higher data storage requirements. CSV is more compact as it doesn’t use metadata.

• Data type support

JSON supports a wider array of data types, including booleans, strings, numbers, objects, and arrays. CSV primarily supports text and data.

• Ease of parsing

JSON requires more processing, but has better support for data types, making it easier to represent complex structures. CSV is faster to process but lacks support for data types, making it harder to represent complex structures.

• Extensibility

JSON can be easily modified to add new data types and objects without breaking compatibility. Adding new columns to CSV files can cause issues.

• Compatibility

Both are nearly universally compatible with modern software.

• Use cases

JSON is best for communicating with APIs and servers, or when used for applications where complicated data types and structures are required. CSV is best for large-scale data exports and imports, spreadsheets, and data analysis.

• Data interchangeability

JSON is better for exchanging complex data structures between computers. CSV is best for simple data without too many qualities.

Can JSON and CSV be Used Together?

Yes, JSON and CSV are often used together when moving data between systems. For example, APIs may require JSON files for communication and return a similar file format. But, if your intended use of the data is for analytical purposes, transforming all that information into CSV might be a good option.

Additionally, if the information retrieved is simple, you could convert a JSON to CSV for data storage purposes. In some cases, if you use something like NoSQL, you can even convert CSV back to JSON to upload it to the database.

Therefore, there are plenty of great use cases to use both formats in tandem. You only need to take care when converting files as complex data types may be hard to represent in CSV.

Alternatives to JSON and CSV

While JSON and CSV are likely some of the most popular formats, there’s plenty of other great options available. The choice only truly depends on your use case.

• XML

Extensible Markup Language is a format that’s amazing at handling highly complicated data while maintaining a neat structure. Yet, at the same time, XML files are a lot less space efficient than either JSON or CSV.

• YAML

YAML Ain’t Markup Language is a data serialization format often used for configuration files such as for Docker or Kubernetes. YAML maintains readability while also being relatively efficient at data storage, however, it’s less widely supported than JSON.

• Avro

Highly efficient data serialization format that’s suitable for big data applications. While associated with Apache Hadoop, it can be used in numerous applications. Its efficiency, however, makes it less readable.