Top 7 Concepts to Know When Using MongoDB as a Beginner

MongoDB is a NoSQL database that uses a flexible, document-based approach to store data in a JSON-like document organised into collections. What this means, in short, is that when working with MongoDB, your data is stored in a way that looks a lot like JSON. This makes it easy to read, easy to map to code, and scalable. MongoDB is modern and follows a different approach than relational databases. Relational databases store data using rows in tables. This approach has its advantages, but one of its major challenges is creating relationships using extra tables. MongoDB solves this problem by introducing a flexible schema approach where data is stored in collections and not tables. This means that data related to an object can be stored together with the object. In this tutorial, we will explore seven concepts that a beginner should know when using MongoDB. With that said, let's get started.

Understanding NoSQL and Document Databases

When most people think of databases, they think of SQL which stores data using rows, tables, and relationships. This is great for many applications, especially when the application data is structured. But it can feel restrictive when the data doesn’t neatly fit into a table structure.

That’s where NoSQL comes in. NoSQL simply means “not only SQL.” It covers a family of databases that move away from the rigid table model to offer more flexibility. Instead of forcing all your data into predefined columns, you can structure it in a way that matches how your application actually works.

MongoDB is a document database, one of the most popular types of NoSQL databases. Instead of rows and tables, data is stored as documents in collections. Each document looks a lot like JSON, with key-value pairs, arrays, and even nested objects. This makes it super easy to represent real-world data. A good example can be a user profile with addresses and preferences in a single record, without having to spread it across multiple tables. Let's take a look at what a user profile with addresses and preferences will look like in a single record in MongoDB.

{
"_id": "u123",
"name": "Moses Anu",
"email": "moses@example.com",
"addresses": [
{ "type": "home", "city": "Abuja" },
{ "type": "work", "city": "Lagos" }
],
"preferences": {
"language": "English",
"theme": "dark"
}
}

MongoDB Architecture Basics

To work with MongoDB effectively, one needs an understanding of the organization of data in a MongoDB database. Unlike SQL databases, where data is structured in tables and rows, MongoDB uses collections and documents.

• Database: Think of it as the container for everything. Just like you might have multiple databases in MySQL, MongoDB also gives the flexibility of multiple databases for different projects or applications.
• Collection: Inside each database, you have collections. Think of a collection like a table with a very flexible schema. This is because MongoDB tables don't enforce a strict schema. You can drop documents with different fields into the same collection.
• Document: This is where the real magic happens. A document is a JSON-like object (actually, BSON under the hood) that holds your data. Documents can have nested objects and arrays, making them much more natural to work with in modern apps. We will talk more on BSON in the next paragraph.

To better understand MongoDB architecture, let's take an example. Let's say we were building a simple e-commerce app using MongoDB. Our data would follow the following structure.

• Have a database called shopDB.
• Inside it, a collection named products.
• And inside that collection, documents representing each product with details like name, price, and category.

The flexible schema

To better understand the concept of a flexible scheme, from our example above, know that a product can have a discount field, while another would not.  MongoDB doesn’t force every product to have the exact same fields. This flexibility is a big reason developers love MongoDB, especially when building applications that evolve quickly.

BSON and Data Types

If you’ve ever worked with JSON, MongoDB will feel a lot more natural. In reality, MongoDB doesn’t actually store your data as plain JSON. MongoDB uses something called BSON. BSON stands for Binary JSON.

What is BSON, and why does MongoDB use it?

BSON is like JSON’s smarter, more efficient cousin. It keeps the same readable, key-value structure that developers love, but adds a few superpowers:

• It supports more data types than JSON (like dates, decimals, and raw binary).
• It’s optimized for fast encoding/decoding, which makes MongoDB queries run faster.
• It stores everything in a compact binary format, which is more efficient for large amounts of data.

Basically, BSON gives MongoDB the flexibility of JSON plus the performance and precision databases needed.

Common BSON data types

MongoDB supports a wide range of data types, but here are the most common ones and their examples:

• String :"name": "Alice"
• Number : "age": 28
• Boolean : "isActive": true
• Date : "createdAt": ISODate("2025-08-18T10:00:00Z")
• Array : "skills": ["MongoDB", "Node.js", "React"]
• Embedded document. Nested objects inside documents:

"address": { 
  "city": "Lagos", 
  "country": "Nigeria" 
}

• ObjectId:  special ID type MongoDB uses for unique document IDs. ObjectId("64f5c9e8a5f76b1d2c3a4567")

Let's take a look at an example user profile document in BSON format stored in MongoDB:

{
  "_id": ObjectId("64f5c9e8a5f76b1d2c3a4567"),
  "name": "Moses",
  "age": 30,
  "isActive": true,
  "skills": ["Laravel", "MongoDB", "Vue"],
  "address": {
    "city": "Abuja",
    "country": "Nigeria"
  },
  "createdAt": ISODate("2025-08-18T10:00:00Z")
}

A few things we should take note of:

• The _id field is automatically generated as an ObjectId if you don’t set it yourself.
• Dates are stored as ISODate, not just plain strings.
• The nested address document and the skills array show MongoDB’s flexibility compared to flat SQL rows.

Schema Design Principles

MongoDB lets you evolve your data model as your application grows because it has a flexible schema. However, without proper schema design, MongoDB's flexible schema can quickly turn messy. You should not throw structure out the window because MongoDB provides a flexible schema. Let's go over a few schema design principles to use when working with MongoDB.

Flexible schema vs. schema-less

MongoDB is often described as schema-less, but that’s a bit misleading. Every document does have a schema—it’s just not enforced at the database level unless you choose to add validation rules.

For example, two documents in the same users collection could technically look very different:

// Document 1
{ "name": "Alice", "email": "alice@example.com" }

// Document 2
{ "username": "Bob123", "age": 29 }

This flexibility can be great during rapid prototyping, but in production, you’ll usually want some consistency. That’s where JSON Schema validation comes in. You can enforce certain rules so you don’t end up with messy, unreliable data.

Embedding vs. referencing documents

Another important decision in MongoDB schema design is how to model relationships. Let's look at some of the ways relationships can be modeled in a MongoDB document.

Embedding (nesting data inside a document)

MongoDB supports embedding documents in a collection. This approach is best used for data that’s accessed together. An example can be a blog post with its comments stored directly inside it. The following is an example of embedding data.

{
  "title": "Intro to MongoDB",
  "comments": [
    { "user": "Alice", "text": "Great post!" },
    { "user": "Bob", "text": "Very helpful." }
  ]
}

Data stored this way has a better performance, and needs fewer queries to retrieve or work with. If not properly structured, it can also lead to bloated documents.

Referencing (linking between documents)

In situations where the size of the related data grows large or is reused often, it is advisable to link data between documents. This is known as referencing.

An example can be a blog post with many comments. In this situation, we can store only the IDs of comments, and the actual comments live in their own collection. The code below is an example of this approach.

// Blog post
{ "title": "Intro to MongoDB", "commentIds": [ObjectId("..."), ObjectId("...")] }

// Separate comment document
{ "_id": ObjectId("..."), "user": "Alice", "text": "Great post!" }

Structuring our data this way keeps documents smaller, avoids duplication, and makes code maintainable. However, it requires multiple queries (though you can use $lookup joins in aggregation).

Avoiding common schema design mistakes

Over-embedding: Because MongoDB has a flexible schema, it can be easy to stuff everything into one giant document. This is not advisable and can lead to bad performance. MongoDB enforces a maximum file size of 16MB per document. This is to ensure that developers properly structure data into documents.

Ignoring indexes: Do not ignore indexes when working with MongoDB. If your queries don’t match your schema design, you’ll end up with slow lookups. Always design with access patterns in mind.

Treating MongoDB like SQL: It's OK to break up data into small documents. However, understand that MongoDB has a flexible schema, and do not treat it like a relational database. If you just copy your relational schema 1:1 into MongoDB (lots of small, normalized tables/collections), you lose the advantages of document modeling.

Take time to plan your schemas. A thoughtful schema can greatly affect your applications performance.

Data Validation

MongoDB is flexible. This can easily lead to unstructured and unorganised data if care is not taken. This is where data validation comes in.

Think of validation as setting ground rules for your collections. It makes sure that any document inserted or updated follows certain rules, like requiring an email field or making sure “age” is always a number.

Using JSON Schema validation in MongoDB

MongoDB supports JSON Schema for validation. JSON Schema is a standard way to describe the shape of your documents. Some rules that can be enforced include:

• Required fields.
• Data types (string, number, date, etc.).
• Value ranges or string patterns.

Data validation can give us the best of both worlds when working with MongoDB. MongoDB’s flexible schema plus guardrails helps to keep our data clean.

Enforcing rules to keep data clean

Let’s say we have a users collection. We want to ensure that:

• Every user has a name (string).
• Every user has an email (string, must look like an email).
• If age exists, it must be a number greater than or equal to 18.

Here’s how we could enforce this with a JSON Schema validator:

db.createCollection("users", {
  validator: {
    $jsonSchema: {
      bsonType: "object",
      required: ["name", "email"],
      properties: {
        name: {
          bsonType: "string",
          description: "must be a string and is required"
        },
        email: {
          bsonType: "string",
          pattern: "^.+@.+\\..+$",
          description: "must be a valid email address"
        },
        age: {
          bsonType: "int",
          minimum: 18,
          description: "must be an integer greater than or equal to 18"
        }
      }
    }
  }
})

MongoDB ensures that only the validated data gets saved in the collection. 

CRUD Operations

At the heart of working with MongoDB are the four basic operations: create, read, update, and delete, better known as CRUD.

If you can do CRUD, you can build almost any application. MongoDB makes this simple with a handful of intuitive methods. Let’s walk through them one by one.

Create

To add a new document to a collection in MongoDB, you use the insertOne() method. For example, we can create a new user document inside a user collection using the code below.

db.users.insertOne({
  name: "Alice",
  email: "alice@example.com",
  age: 25
})

MongoDB automatically generates a unique _id for each document created.

Read

Reading data from a collection is straightforward. We just use the find(). We can get all documents or filter with conditions. The code below can find all users from a user collection.

// Find all users
db.users.find()

We can filter using a unique key—an email, for example.

// Find one user by email
db.users.find({ email: "alice@example.com" })

find() always returns a cursor (which you can iterate over). If you just want a single match, you can use indOne().

Update

Once data is created, there must be a need to modify it. To modify data, the ``` updateOne()``` method is used. We specify a filter and the changes to apply.

// Update Alice’s age
db.users.updateOne(
  { email: "alice@example.com" },
  { $set: { age: 26 } }
)

The $set operator updates only the specified field, leaving the rest of the document unchanged.

Delete

Finally, we can remove documents with deleteOne() or deleteMany(). The example below shows a deleteOne action.

// Delete Alice’s record
db.users.deleteOne({ email: "alice@example.com" })

Note that deletes are permanent.

With just these four operations, you can build everything from to-do apps to e-commerce platforms.

Security Best Practices

Security should never be an afterthought when it comes to databases. With that said, let's look at the core best practices every beginner should know.

Authentication and authorization

By default, a fresh MongoDB installation may not enforce authentication. That means anyone who can connect could potentially access your data. This can be a huge risk if not taken care of. To make sure our MongoDB is not left open, we can implement authentication and authorization.

• Authentication ensures that only valid users can connect.
• Authorization ensures that even authenticated users can only perform the actions they’re allowed to.

For example, instead of logging in as a root/admin user everywhere, you should create dedicated users for specific tasks.

// Create a new user with read-only access
db.createUser({
  user: "reportViewer",
  pwd: "strongPassword123",
  roles: [ { role: "read", db: "salesDB" } ]
})

Now, this user can only read data from salesDB not write or delete.

Role-based access control (RBAC)

MongoDB uses roles to manage what users can do. Some built-in roles include:

• Read: Users with this privilege can only view data.
• ReadWrite: Users with this privilege can view and modify data.
• DbAdmin: Users with this privilege can manage indexes, stats, and profiling.
• Root: Users with this privilege have full control. Avoid using this for day-to-day work!

Following the principle of least privilege gives a user the required permissions they need to do their job, while ensuring the security of the MongoDB database.

Avoid public exposure

One of the most common beginner mistakes is leaving MongoDB listening on the default port (27017) and binding to 0.0.0.0 (all network interfaces).

That means your database is open to the entire internet. To prevent this, you should take the following measures:

• Bind MongoDB only to localhost or a private/internal IP.
• Use a firewall (or cloud security group) to restrict access.
• Always connect through a VPN or SSH tunnel if remote access is needed.

# Example: /etc/mongod.conf

net:

  port: 27017

  bindIp: 127.0.0.1   # Only accessible locally

With these practices in place, your MongoDB deployment will be much safer against accidental leaks or malicious attacks.

Conclusion

MongoDB has earned its place as one of the top databases for modern applications because of its flexibility and power. Whether you’re tinkering with a side project or managing a large-scale system, having a solid grip on the fundamentals will save you a ton of time and help you be more productive. In this article, we explored the following seven key concepts a beginner should know when working MongoDB. They include

• Understanding NoSQL and document databases.
• MongoDB architecture basics.
• BSON and data types.
• Schema design principles.
• Data validation.
• CRUD operations.
• Security best practices.

A good understanding of the concepts above will give you a strong foundation when working with MongoDB. For more details, make sure to check out the MongoDB Docs and DataCamp's Introduction to MongoDB in Python course.