MongoDB vs DynamoDB: Which NoSQL database is right for you?

Data storage flexibility can be one of the most important keys to your productivity as a software developer, and NoSQL databases are a great facilitator of that flexibility. They allow for high-speed data management and scalability, which makes them ideal for a variety of data structures and modern use cases, such as web applications and cloud-based services.

Today, we’ll go over the pros and cons of two services and discuss which you should use depending on your project goals and backend use cases. Given the number of NoSQL database services available, we’re narrowing our focus to two of the most popular: MongoDB and Amazon’s DynamoDB.

We’ll cover:

• What Is a NoSQL database?
  • Types of NoSQL databases
• What Is MongoDB?
• What Is DynamoDB?
• Key differences
• Wrapping up and next steps

What is a NoSQL database?#

NoSQL stands for “non-SQL” or “not only SQL.” Structured Query Language (SQL) is a programming language developed in the early 1970s to allow programmers to interface with relational database management systems (RDBMS). This interface allowed them to organize data into tables that were then linked based on shared data.

In the era of Big Data, however, increasing demand for faster and more disparate use of large data sets led to the development of NoSQL or non-relational databases. Without the need to connect tables, developers could use NoSQL databases to divide search-query and data-storage workloads across multiple database servers, instead of relying on a single server. NoSQL databases have not rendered relational databases obsolete but simply provided a viable alternative for relevant use cases.

NoSQL databases are also considered distributed, meaning that data is stored on multiple local or remote servers. This means that, if some piece of the database is taken offline, the rest of the database remains functional and accessible.

Types of NoSQL databases#

Broadly speaking, there are four major types of NoSQL databases:

• Key-value: Considered the simplest type, this is a schema-less data model that houses data in a dictionary of key-value pairs, meaning each item has a key and a value. You often see this kind of database used in web applications that need to store and cache user-session data, such as for gaming or shopping apps.
• Document-oriented: This model stores data as documents, usually in JSON, XML, or BSON (binary JSON) formats, with each document containing pairs of fields and values. These values come in various types, such as arrays, Booleans, numbers, objects, and strings. This type ensures that data stays together when used in applications. It also gives you increased flexibility, as you don’t need to make sure data schemas are uniform across all documents.
• Wide-column: This database arranges data into columns, which allows you to access only the data you need without wasting memory. This method of organization is considered more complex than other types of NoSQL databases. These can be useful for storing data across multiple servers.
• Graph: This model organizes data in nodes and edges. Nodes are any person, place, or object in the database, and edges define the relationship between nodes. For example, if you were to put your family tree in a graph database, an edge could categorize the relationship between you and your brother as “siblings.”

What is MongoDB?#

MongoDB is an open-source, document-oriented NoSQL database from MongoDB Inc. that began development in 2007. It uses BSON-formatted documents and files to store and manage data. MongoDB supports field, range query, and regular-expression search functions through this system.

Like other document-oriented databases, MongoDB doesn’t require matching data schemas across documents, which can provide flexibility that other databases lack. In addition, MongoDB Atlas is cloud-agnostic, meaning you could feasibly deploy it on any cloud provider (AWS, Google Cloud, or Microsoft Azure) or on premise without much issue. MongoDB is also compatible with a wide range of programming languages, including C, C#, Python, Ruby on Rails, Rust, and Scala, increasing provisioning options.

MongoDB is ideal if your project might need to scale quickly to meet increasing demands. Being schema-less, MongoDB supports adding document data to the database rapidly, which can speed up development. This is great if your project’s schema is prone to frequent changes, as it means you don’t have to reformat every document in the database after every change.

MongoDB is not without its drawbacks. Its schema-free design is flexible but also means you might experience some difficulty performing complex data transactions. In some cases, this can lead to data loss or corruption. And its document size is limited to 16MB, meaning larger data sets have to be broken up to fit in the database properly.