What is MongoDB?

So, what is MongoDB? MongoDB is classified as a document-oriented NoSQL database, that term is usually used to refer to any non-relational databases. For a bit of clarity, the term “NoSQL” stands for “non-SQL” or “not only SQL.” The difference being, NoSQL as opposed to SQL, is considered an approach to database management. SQL is just a query language. However it is very similar to the query languages used by NoSQL databases.

Non-relational Databases VS Relational Databases

So what’s the difference? For those familiar with SQL, you may have heard of a RDBMS (Relational Database Management System). They use a tabular data structure, with data that is represented as a set of rows and columns, which makes the model great for structured data. Now looking at Non-relational databases, depending on the type of NoSQL database used, the data models may vary. For example, you will see key-value, document, graph, and wide-column depending on the service you use. This detail alone makes this model optimal for semi-structured and unstructured data.

Data Model? Document-oriented!

As mentioned in the previous section, the type of data model can vary depending on the type of database used. Luckily today we will be able to zero in on the document-oriented side of NoSQL databases since we are talking about MongoDB. A document-oriented database stores data in documents that are similar to JSON (JavaScript Object Notation) objects. Each one of these documents hold pairs of fields and values. The values that the documents hold can be a wide variety of types, which include strings, numbers, arrays, booleans, and even other objects.

What about Organization?

Up to this point we know that NoSQL databases are the go to for semi-structured and non-structured data, MongoDB stores it’s data in documents similar to JSON, and these documents can hold a variety of values and fields. While all of this sounds great, we still need to know one thing, how is this data organized, what’s the structure?

Organization Explained Let’s hunker down on the organization structure. We know about documents in general. They store information about one object and any of it’s related metadata in field-value pairs. The value can be a variety of data types. And some additional information, documents can be stored in formats like JSON, BSON (Binary JSON), and XML.

Here’s an example of a JSON document so that you can get a visual (Provided by MongoDB.com):

Organization Continued - Collections Now that we’ve seen and know what a document is, let’s talk about collections. To put it simply, collections are just a group of documents. A collection in MongoDB is similar to a table in relational-databases. They’re made specifically to hold documents, and provide structure to data. So to lay out everything, databases contain collections, collections contain documents, and documents contain field-value pairs where the field is the description of the value, and the value being a data type, which again can be strings, numbers, arrays, booleans, and even other objects.

Structure visual (Provided by GeeksforGeeks):

Key Features of MongoDB?

Reasons why you may use it in your next project:

Document Model: Data is stored in documents, and documents map directly to objects used in most popular programming languages, making them much more natural to work with. And in this model the data that is accessed together, is stored together. Making it so developers have less code to write, and users get higher performance. 1.

Flexible schema: Document-oriented databases have flexible schema. MongoDB is often referred to as schema-less. What this means is not all documents in a collection need to have the same field. Collections can store documents with different structures, fields are not predefined, which allows for dynamics updates, and is most suitable for applications where data is expected to change frequently. 1.

Ease of Use: Developers report that working with data in documents is easier and more straightforward than working with data in tables, but why is this? Documents map directly to data structures used in most popular programming languages, there’s no need to split related data across many tables when storing information or join it back together when it’s time to retrieve the data, and developers don’t need a separate ORM to handle data manipulation for them. All data can be worked with directly in their applications.

Use Cases/Success Stories

Now that we know so much about MongoDB, the very popular NoSQL database, what can or should you use it for in your upcoming projects?

Popular Use Cases

Cloud Data Strategy: Check out this article on how MongoDB was utilized to enhance application development on Google Cloud Platform’s Compute Engine. 1.

Generative AI: As companies hurry to adopt their own version of generative AI, they risk potentially opening up their users to security risk if best practices aren’t followed. Learn how Cisco used MongoDB to create a secure data environment that enable their customers to innovate faster with generative AI here. 1.

Content Management: Whether you’re solo developer needing a way to manage content on their site, or a journalism giant like Forbes, MongoDB is the solution. Learn how Forbes realized they needed to ditch their primary server for all published content (CMS) for a more modern solution like MongoDB here.