What is MongoDB and How Does It Work?

MongoDB’s highly scalable platform makes it a powerful choice for handling unstructured data, real-time analytics, and large-scale distributed applications.

Additionally, other NoSQL options offer such robust features for high-throughput workloads, caching, and real-time data processing.

However, if you’re unfamiliar with NoSQL databases, it may be unclear how MongoDB differs from traditional relational databases and why it might be the best fit for your business.

To help you understand what MongoDB is and how it works, this guide explains:

• The key differences between relational databases and MongoDB
• MongoDB’s features, core products, and plans
• Three top MongoDB alternatives and their unique strengths
• How to secure the MongoDB at a low price with fair terms

What is MongoDB?

MongoDB is a NoSQL database platform designed for flexibility and scalability. Unlike traditional SQL databases that rely on structured tables and rows, MongoDB stores data in a document-based format.

This structure allows you to handle large amounts of unstructured or semi-structured data faster and easier, which supports applications like real-time analytics, content management systems, and scalable search.

Below, we’ll compare MongoDB to traditional SQL-style databases, like Airtable and Microsoft Access, and explore why its non-relational model offers key advantages for certain business use cases.

NoSQL vs SQL databases

While both SQL (relational) and NoSQL (non-relational) databases are designed to store and manage data, the core difference lies in their approach to structure and scalability.

SQL databases are ideal when your data is structured and requires consistency. They use predefined schemas, meaning the format of the data must be known in advance. 

This makes a tool like Airtable highly effective for use cases that rely on clear relationships between data points, such as managing inventories or handling task workflows where consistency and organization are essential.

In contrast, NoSQL databases like MongoDB prioritize performance over rigidity. MongoDB doesn’t require a predefined schema and stores data in document-based formats, allowing for diverse and dynamic data structures.

The flexibility of MongoDB’s NoSQL database is especially useful when working with data that doesn’t fit neatly into rows and columns, or when handling large and rapidly changing datasets.

For example, in real-time analytics, data might come from various sources and change frequently, making MongoDB’s adaptable structure more suitable. 

Similarly, systems that manage dynamic content—such as user submissions or complex metadata—benefit from MongoDB’s ability to handle varied data types without relying on rigid table relationships.

Another advantage of NoSQL databases like MongoDB is their ability to scale horizontally. 

As your data grows, MongoDB can easily distribute data across multiple servers without the performance limitations typically seen in relational databases. This makes MongoDB an ideal solution for businesses requiring high availability and performance at scale.

By offering flexible data management, schema-less storage, and powerful scalability, MongoDB works effectively in use cases where SQL databases might struggle to keep pace with dynamic, unstructured data and the need for rapid growth.

Are you exploring how to shift existing relational databases to a NoSQL database? MongoDB offers a tool for migrating and transforming data to MongoDB while simplifying architecture and generating necessary code from your new data model. Additional migration support is available through dedicated support and certified technology partners.

A detailed overview of how MongoDB works and its key features

MongoDB is designed for flexibility, scalability, and performance. Here’s a look at how the platform functions and the features that make it a powerful choice for managing unstructured or semi-structured data.

Document data model and schema flexibility

MongoDB’s document-based model uses BSON (Binary JSON), which gives you flexibility by accommodating a wide range of data types, such as arrays, nested objects, and more complex structures. 

Its schema-less design allows you to easily handle data from multiple sources or formats, whether you're integrating customer feedback, product catalogs, or real-time analytics, all without the need for constant adjustments.

Horizontal scaling and replication

MongoDB lets you scale horizontally by distributing your data across multiple servers (sharding). It also ensures high availability by replicating data across different servers, so your application can continue running smoothly even during failures.

Aggregation framework

With MongoDB’s aggregation framework, you can process and transform your data in real-time.

This allows you to filter, analyze, and generate reports from large datasets, whether you're tracking customer behavior, monitoring sales trends, or generating financial summaries. These capabilities help you uncover patterns, identify operational inefficiencies, and make data-driven decisions more effectively.

MongoDB UI: Compass and Atlas

MongoDB provides intuitive graphical user interfaces (GUIs) that make managing databases more accessible.

Compass allows you to visually explore your data, offering insight into collections, documents, and schema analysis. You can use it to manage data structures, create queries, and visualize relationships between data.

Atlas, MongoDB’s cloud-based platform, offers a GUI for managing clusters, automating backups, scaling, and security monitoring.

Both solutions enhance your ability to deploy, manage, and maintain databases, ensuring that infrastructure tasks are simplified for non-technical users.

Indexing and performance optimization

MongoDB’s indexing methods are key to improving query performance, especially as your datasets scale. By creating efficient indexes, you ensure faster query execution. 

With Compass, you can not only monitor your indexing setup but also visualize index performance in real-time. This helps you identify underperforming queries, adjust indexing strategies, and fine-tune your database to ensure optimal performance as your data grows.

MongoDB products, pricing, and plans

MongoDB has three core products, each designed to meet different use cases, from small projects and startups to enterprise-grade applications that require managed services or self-hosted solutions:

1. MongoDB Atlas: A fully managed cloud database service that automates deployment, scaling, and operations for MongoDB databases on cloud platforms like AWS, Azure, and Google Cloud. Atlas is MongoDB’s flagship cloud service.

2. Enterprise Advanced: A commercial, self-managed version of MongoDB with advanced security, monitoring, and backup features. It’s suitable for businesses needing compliance and security for large-scale deployments, whether on-premises, in the cloud, or in hybrid environments.

3. Community Edition: The free, open-source version of MongoDB. This is widely used by developers and small businesses for non-enterprise needs, offering the basic features of MongoDB without the advanced tools of Atlas or Enterprise.

Pay-as-you-go pricing

For MongoDB’s Atlas service, users typically pay based on usage, including data storage, throughput, and cloud provider selection (such as AWS, Azure, and GCP).

Vendr’s MondoDB pricing guide provides further detail on how the platform’s pricing scales with team size and usage:

• For a team of 200, costs range between $13,700 and $29,600. 
• For larger teams of around 1,000, prices increase to $54,700 up to $137,200. 
• For even larger organizations, prices range from $175,500 to $262,500.

For MongoDB Atlas, pricing scales similarly:

• A 200-person team can expect costs between $20,700 and $37,500, while teams of 1,000 are estimated to pay $35,900 to $94,400. 
• Larger teams might pay between $29,000 and $436,100. 

Note: The pricing for Pro and Enterprise tiers is listed at $1, but it's recommended to contact MongoDB directly for a custom quote, as this pricing may vary based on your needs.

MongoDB vs top alternatives

Here are some of the alternatives you may wish to consider before committing to MongoDB.

Redis

Redis is an in-memory data store known for its speed and is often used for caching and real-time applications like chat apps or gaming leaderboards. It’s also gaining traction in vector search, making it useful for AI-powered applications. However, Redis is less suited for long-term data storage compared to MongoDB. Buy Redis with Vendr.

Apache CouchDB

CouchDB is a document-based NoSQL database designed for easy data access and synchronization across distributed systems. It allows for smooth data exchange over web protocols and is ideal for offline-first applications, ensuring data syncs reliably once reconnected, making it valuable for mobile and remote environments.

Amazon DynamoDB

DynamoDB is a fully managed, serverless NoSQL service that excels in high-throughput workloads on AWS. It scales automatically but lacks MongoDB’s flexibility for on-premises or hybrid deployments.

Get the best deal on MongoDB and save time on negotiations with Vendr

Vendr helps you secure low pricing and fair terms on MongoDB by utilizing expert contract analysis and skilled negotiators. 

Our team leverages factors like user volume, contract duration, and feature upgrades to ensure you’re getting maximum value for your investment.

We streamline the procurement process, cutting down on time-consuming negotiations and removing unnecessary sales calls, so you can focus on your core operations without adding to your workload.