Why Choose MongoDB for Relational Data Structures?

Hey everyone! Need some real-world perspectives here, if you don't mind sharing 🙏

I'm building along with DevFlow. The data feels pretty classic relational: users → questions → answers → votes, plus tags and joins. JS Mastery used MongoDB + Mongoose, and the schema ends up looking very SQL-ish (with refs, required fields, populate, etc.). What keeps bugging me is this: technically both SQL (e.g., Postgres) and NoSQL (e.g., MongoDB) can handle these patterns. But beyond “both work,” why do we really pick one over the other?

Does the actual shape of the data matter that much?,
Or is it more about how we query and scale it?,
And in real projects: do the “cons” of forcing MongoDB to act relational actually show up — or do people happily build large-scale relational-ish apps on NoSQL without issues?,

I’m genuinely trying to go past surface-level answers and see why someone experienced (like JSMastery) might still choose Mongo over SQL in a clearly relational project:

Was it developer speed?

Familiarity?

Or is there a hidden advantage I'm missing?

If you’ve actually shipped something similar: What made you pick one? Did it save you pain later, or add hidden complexity? Really appreciate any thoughts! Trying not to settle for “it works,” but to understand why it fits. Thanks! 🙌

Developer Speed

One significant advantage of using MongoDB is its ease of use and rapid development capabilities. With MongoDB, you can quickly create and populate your database without having to worry about complex schema designs or SQL queries. This can be particularly beneficial for projects with tight deadlines or rapidly changing requirements.

Hidden Advantages

In addition to these benefits, MongoDB offers several hidden advantages that can make it a better choice for relational data structures. For example:

Scalability: MongoDB is designed to scale horizontally, making it easier to handle large amounts of data and high traffic. This can be particularly important for projects that expect rapid growth or high traffic.

Flexibility: MongoDB's flexible schema design allows for easy adaptation to changing data structures and requirements. This can be particularly beneficial for projects with rapidly changing data models.

Real-time Data Processing: MongoDB's built-in support for real-time data processing and aggregation makes it an excellent choice for projects that require fast data processing and analysis.

When to Choose MongoDB

So, when should you choose MongoDB for your relational data structures? Here are a few scenarios where MongoDB might be a better choice:

Rapid Development: If you need to quickly develop a project with a relational data structure, MongoDB's ease of use and rapid development capabilities can be a significant advantage.

Scalability: If your project expects rapid growth or high traffic, MongoDB's horizontal scalability can help you handle the increased load.

Flexibility: If your project has rapidly changing data structures or requirements, MongoDB's flexible schema design can help you adapt to these changes.

In conclusion, while both SQL and NoSQL databases can handle relational data structures, MongoDB offers several advantages that can make it a better choice for certain projects. By considering factors such as developer speed, familiarity, and hidden advantages, you can make an informed decision about which database technology is best for your project.