Feature image MongoDB Data Modeling

MongoDB Data Modeling: Meaning, Pros & Cons and Prospective

By Brian Laleye · October 31, 2022 · 6 min read

​​MongoDB is a powerful document-oriented database system that has gained popularity in recent years for its ease of use and scalability.

MongoDB is often used as a “NoSQL” solution when traditional relational databases such as MySQL are not suited to the needs of the application.

Let’s dive into what MongoDB Data Modeling means for your business, its benefits and why it’s gaining popularity.

What is MongoDB Data Modeling?

MongoDB data modeling is the process of designing the structure and schema of data in a MongoDB database.

Data modeling is a critical part of any database design, and MongoDB is no exception. 

A well-designed data model can improve performance, scalability, and maintainability of a MongoDB application.

There are two main aspects to consider when designing a data model in MongoDB: the structure of the data, and the relationships between the data.

The structure of the data is important for performance and “queryability”, while the relationships between the data can be used to enforce data integrity.

The Basics of MongoDB Data Modeling

The most basic element of a MongoDB data model is the document.

A document is a JSON-like data structure that can contain any number of key-value pairs.

Documents are similar to rows in a relational database table, but they are more flexible in that they can contain nested data structures.

A document is stored in a collection. A collection is equivalent to a table in a relational database.

A collection can contain any number of documents, and there is no schema that needs to be enforced.

This makes it easy to add or remove fields from documents as needed.

MongoDB also supports references between documents.

A reference is a field in a document that contains the _id of another document. 

References are used to establish relationships between documents.

For example, if we have a collection of users, each user document might contain a reference to the document of the user’s spouse.

In this way, we can easily look up information about a user’s spouse without having to query the entire database.

Benefits of MongoDB Data Modeling

One of the main benefits of using MongoDB is that it is very easy to scale.

MongoDB is designed to be horizontally scalable, meaning that it can easily be scaled up to handle more data by adding more servers.

This is in contrast to relational databases, which are designed to be vertically scalable, meaning that they can only be scaled up to a certain point before they need to be replaced with a larger database.

Another benefit of MongoDB is that it has a flexible schema.

As mentioned above, documents in a MongoDB collection can have any structure, and the structure can be different for each document.

This makes it easy to add or remove fields as needed, without having to make changes to the database schema.

This flexibility can be a great advantage when the data model is likely to change over time.

Another advantage of MongoDB is that it supports indexing.

This means that fields in a document can be indexed to make queries more efficient. 

Indexing is not possible with a relational database, which can make MongoDB a better choice when dealing with large amounts of data.

Drawbacks of MongoDB Data Modeling

One of the main drawbacks of MongoDB is that it does not support joins.

Joins are a way of combining data from multiple tables in a relational database.

This can be a useful way of querying data, but it is not possible with MongoDB. This means that data from multiple collections must be queried separately and then combined in the application code.

Another drawback of MongoDB is that it can be difficult to enforce data integrity.

Data integrity is the concept of ensuring that data is consistent across different parts of the database.

In a relational database, this is typically done with foreign keys and constraints. 

MongoDB does not have any built-in mechanisms for enforcing data integrity, so it must be done in the application code.

This can be difficult to do correctly, and can lead to data inconsistencies if not done carefully.

One way to work around the lack of data integrity enforcement in MongoDB is to use a document validation tool like JSON Schema.

JSON Schema can be used to validate data before it is inserted into the database, and can be used to enforce data integrity constraints.

However, JSON Schema is not a perfect solution, and it can be difficult to use correctly.

The Future of MongoDB Data Modeling

MongoDB is a popular database system that is well-suited for many applications.

It is easy to use and scalable, and has a flexible schema that can be a great advantage when the data model is likely to change over time.

However, it does have some drawbacks, such as the lack of support for joins and the difficulty of enforcing data integrity.

Despite these drawbacks, MongoDB is still a popular choice for many applications, and its popularity is likely to continue to grow in the future.

As the MongoDB community continues to grow and develop, new tools and techniques will be created that make it even easier to use MongoDB for a variety of applications.

So, if you’re thinking about using MongoDB for your next project, don’t let the drawbacks hold you back. With a little bit of effort, you can overcome them and take advantage of all that MongoDB has to offer.


At RestApp, we’re building a Data Activation Platform for modern data teams with our large built-in library of connectors to databases, including MongoDB, data warehouses and business apps.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

Play Video about Analytics Engineers - Data Pipeline Feature - #1

Discover Data modeling without code with our 14-day free trial!



Subscribe to our newsletter

Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.

Build better data pipelines

With RestApp, be your team’s data hero by activating insights from raw data sources.

Activate and combine any data sources without code

Transform your data with our No Code SQL, Python and NoSQL functions

Run automatically your data pipelines and sync modeled data with your favorite tools

Share your pipelines and collaborate smarter with your teammates

Discover how Data Transformation really means

Find out the new data architecture concept of Data Mesh

Learn how Operational Analytics actives your data

Learn how to deliver great customer experience with real-time data


Crunch data at scale with ease

Configure connectors, no build

Save time & efforts for data prep

Save time & efforts for data prep


Stay always up to date on data activation

Get access to tips and tricks to model your data

Discover our always evolving and regularly updated documentation

Find out how we keep your data safe