MongoDB vs SQL Databases: What’s the Difference?

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MongoDB vs SQL

Key Takeaways

  • MongoDB is a NoSQL database widely in use today.
  • SQL databases have been around for decades as the standard for relational database systems.
  • SQL prioritizes organization and consistency, whereas MongoDB allows for dynamic data management.
  • MongoDB is ideal for horizontal scaling, while SQL is oriented towards vertical scaling.
  • MongoDB sacrifices transaction capability and data integrity for a more dynamic database experience.
  • SQL is optimal for data integrity and ensuring consistency in databases.
  • MongoDB and SQL are popular among developers, with many leveraging both for different purposes.
  • MongoDB and SQL are acclaimed databases that offer unique features tailored to specific projects and developer preferences.

Databases are the building blocks of every software. They allow us to store, organize, manage, and retrieve data without much manual effort. Software and applications need a database to function. Everything from a simple mobile widget to a complex information system uses databases.

Even among databases, many different types are available for developers. Knowing what separates the different databases is an essential skill for any programmer. This blog will examine a prominent debate in the database landscape to explain the differences between various databases. The debate in question is, of course, MongoDB vs SQL.

These names are famous in the programming world today, and many different database management systems are based on their foundation. By understanding the core difference between MongoDB and SQL, developers can use these databases to their maximum potential. Without further ado, let’s dive into the wonderful world of databases and learn precisely how our favorite applications work!

Table of Contents

  1. Key Takeaways
  2. Introduction to MongoDB
  3. Introduction to SQL
  4. Advantages and Disadvantages of MongoDB
    1. Advantages of MongoDB
    2. Disadvantages of MongoDB
  5. Advantages and Disadvantages of SQL
    1. Advantages of SQL
    2. Disadvantages of SQL
  6. Key Differences Between MongoDB and SQL: A Detailed Comparison
    1. Data Structure
    2. Query Language
    3. Scalability
    4. Transactions
    5. Use Cases
  7. MongoDB vs SQL: Choosing a Database for Your Next Project
  8. Conclusion
  9. FAQs

Introduction to MongoDB

Introduction to MongoDB

Image Credit: MongoDB

MongoDB was invented by a trio of brilliant minds in 2007 as an alternative to the traditional relational databases dominating the development space. Dwight Merriman, Kevin Ryan, and Eliot Horowitz set off on their own to build a database that could handle the needs of modern applications. The goal was simple: build a database that could handle high data throughput and offer flexibility beyond relational databases.

They founded the company 10gen, which started as an app development platform but later pivoted to solely focusing on its database technology: MongoDB. MongoDB is an open-source NoSQL database management system, which means it subverts the need for relational databases and provides a more flexible, non-structured database querying option.

The company rebranded to MongoDB in 2013, focusing on developing its exponentially popular custom, document-oriented database server.

Since then, MongoDB has been a fundamental part of the Internet. Today, over 4700 websites and applications are powered by MongoDB. Understanding MongoDB vs SQL differences is important because they represent two distinct halves of the Internet and how it works.

Introduction to SQL

Introduction to SQL

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Structured Query Language, or SQL, has been a core ingredient of website and application development since the early 1970s. Even before the advent of the Internet, when computers were mainly used in local networks, SQL was used to power applications and manage databases.

Developed in the early 1970s by Edward F. Cobb, SQL started as a project to practically build Cobb’s theorized relational databases. The purpose was to build a system where data would be stored in tables and be independently accessible without needing to reorganize the tables themselves.

SQL, originally called SEQUEL (Structured English Query Language), was developed to allow third-party access to relational databases for data management and retrieval. The name was changed to SQL later for trademark purposes. In 1986, SQL became a standard of the American National Standards Institute (ANSI), and the International Organization for Standardization (ISO) adopted it in 1987.

SQL’s standardization has led to the development of many different relational database management systems built on its foundations. MySQL, Microsoft SQL Server, and PostgreSQL are the most popular and influential SQL database systems currently used. While MongoDB’s history is not as exciting, exploring MongoDB and SQL differences is still necessary for a holistic view of the modern programming world.

Also Read: Data Duel: MongoDB vs PostgreSQL – Which Reigns Supreme?

Advantages and Disadvantages of MongoDB

Advantages and Disadvantages of MongoDB

As with most things, there are pros and cons to choosing between two options. MongoDB, the popular NoSQL choice, has several advantages and disadvantages that must be carefully considered before use.

Advantages of MongoDB

Let’s explore some major reasons why MongoDB is so popular among developers and companies.

High Performance

High Performance

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MongoDB offers high performance for both reads and writes by leveraging an in-memory computing style for working with data. The ability to index any field and the existence of embedded documents reduces the need for expensive join operations, which are common in relational databases.

Schema-less Nature

MongoDB does not enforce a strict schema for databases. A schema is the blueprint of the database, which shows how tables in the database are related. Because it is a NoSQL, unstructured database system, data in MongoDB is stored as documents in a collection without requiring the same structure and fields for everything.

This flexibility enables quick development and iteration without worrying about pre-defining the structure. Due to MongoDB’s schema-less nature, migration issues commonly found in relational databases are easily avoided.

Flexible Data Model

MongoDB provides an ideal database environment for applications needing dynamic data management. Scaling companies that want the room to expand and freely manipulate their database make the best use of the flexible model. MongoDB also supports many different data types, allowing developers to choose the optimal choice.

The unstructured, document-based system is perfect for e-commerce applications and content management systems.

Built-in Replication and High Availability

MongoDB’s ability to replicate databases is an incredible advantage in modern times. Websites and applications cannot afford extended downtime, and database servers must always be active. The reality is that things will eventually malfunction and need fixing, but that does not mean the website or application must also go down.

With MongoDB’s built-in replication, companies can ensure their database is always available. Having the same data copied across multiple servers improves data availability and is a strong defense against database corruption or server malfunction.


MongoDB’s sharding capabilities allow for horizontal scalability. Sharding is where the data is distributed and stored across several machines as the dataset grows. Applications with vast datasets or high write loads can maximize efficiency with sharding while keeping it cost-effective compared to scaling vertically.

Want to make use of all these MongoDB features on your Ubuntu machine? Here’s a quick guide to installing MongoDB on Ubuntu to help get you started.

Disadvantages of MongoDB

While incredibly robust, MongoDB has downsides that might sway developers in the other direction. Notable examples include:

Memory Usage

Memory Usage

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MongoDB uses RAM to store working data sets. This dependency can spiral into a big problem down the road as more RAM is needed for an expanding database with high volumes of data. Costs can spike through the roof if the application has to scale rapidly, and not updating RAM can lead to performance issues.

Transaction Support

NoSQL databases initially did not support transactions. Things are different now, and MongoDB currently offers support for multi-document transactions. While the support is now available, it is still not on the same level as relational databases. Being an optional feature only used when necessary; transactions are far less robust and dependable.

This is a tradeoff for MongoDB’s higher scalability and flexibility. Those features come at the cost of less consistency and weaker transaction systems.

Nested Documents and Depth

MongoDB offers limited nested document capability, with document size caps at 16MB. Due to their free-flowing, unstructured nature, managing complex nested documents can become a headache in MongoDB. This limitation can affect overall application performance as the document hierarchy grows deeper and more complex.

Management Tools

MongoDB’s tools are sufficient for the most part but lack the refinement of SQL tools. This lack of refinement is natural, considering SQL has been around for far longer than MongoDB. Developers may find some of MongoDB’s native management and administrative functions lacking. Third-party tools are available for added functionality, but there is room for expansion and refinement as far as the built-in tools go.

Advantages and Disadvantages of SQL

Advantages and Disadvantages of SQL

SQLs rich history is proof of rigorous development and growth over the years. Let’s discuss how far SQL has come and how far it has yet to go!

Advantages of SQL

SQL is a developer-favorite for several reasons, the most important of which are listed below:

Strong Security Features

Strong Security Features

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SQL comes with incredibly robust built-in security features. It provides comprehensive access control, user management, and data encryption capabilities. SQL is ideal for organizations that prioritize data security. Databases containing sensitive information, such as customer details and financial information, are an example of where these features are best utilized.

Data Integrity with ACID Compliance

ACID stands for Atomicity, Consistency, Isolation, and Durability.

ACID compliance is a cornerstone of database management systems that use SQL. It ensures reliable transaction processing and maintains the integrity and accuracy of the data.

  • Atomicity guarantees that each transaction is treated as a single, indivisible unit, meaning that either all of its data modifications are completed or none are. Any changes already made will be rolled back if a transaction is interrupted by a system failure, error, or power loss.
  • Consistency ensures that a transaction can only bring the database from one valid state to another, maintaining all the predefined rules, such as unique keys, foreign keys, and constraints. The entire transaction will be rolled back if it violates these rules.
  • Isolation determines how transaction modifications affect other transactions. It ensures that transactions are securely and independently processed simultaneously without interference.
  • Durability ensures that once a transaction has been committed, it will remain so, even during a power loss, crash, or error. Once the transaction is complete, changes to data are stored permanently and will not be undone.

These four properties ensure that transactions in a database are processed reliably and help safeguard against data corruption, loss, or inconsistent results. Financial systems are a notable example of a system that cannot risk a compromised database. SQL databases are used in such systems to minimize error and maximize consistency.

Standardized Language

SQL is the de facto language used for the majority of relational databases today. Mastering SQL also makes developers proficient in relational database management as a whole. It is a highly valued skill that opens many professional roads for aspiring programmers, data analysts, and developers.

Declarative Syntax for Complex Queries

SQL allows developers to make complex queries without much trouble. It is particularly effective for queries involving joining data from several tables. It is all thanks to SQL’s declarative syntax, which helps specify complex data operations efficiently and elevates database understanding,

Mature Tools and Ecosystem

SQL databases are deeply ingrained in the fabric of software development, largely due to their long history and proven reliability in managing structured data. Their maturity is reflected in the robust ecosystem built around them, which includes comprehensive tools for every aspect of database handling—from design and development to management, querying, and optimization.

Some of the prominent tools for specific tasks are listed below:

  • Design and Development Tools: Oracle SQL Developer, ER/Studio, MySQL Workbench
  • Database Management Tools: Microsoft SQL Server Management Studio (SSMS), Navicat, phpMyAdmin
  • Querying and Reporting Tools: Tableau, Toad, Power BI

Disadvantages of SQL

To properly comprehend the MongoDB vs SQL question, we have to look at both sides of SQL, the good and the bad. Here are some of the challenges that come with using SQL for database management:

Operational Costs

Operational Costs

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SQL servers can get expensive to improve and maintain long-term. When dealing with high data volumes at the enterprise level, licensing, and hardware fees can eat a good chunk of resources. Vertical scaling also makes the server more resource-intensive as time passes, raising costs to keep things running.

Rigid Schema Requirements

As a relational database, SQL requires developers to define the schema before inputting data. This rigidity of structure makes adapting to changes in data structure increasingly difficult. Database migrations and altering tables are needed to facilitate the change, which causes disruption and requires additional resources.

Complexity in Handling Unstructured Data

SQL databases are ill-equipped to manage unstructured data. Emails, videos, social media posts, and other forms of unstructured data are very difficult to manage in SQL. If we are talking about MongoDB vs SQL, the former is the better choice for effectively dealing with unstructured data.

Less Effective for Distributed Systems

SQL systems are less effective at managing distributed database systems. SQL is typically used in centralized databases built for vertical scaling. Vertical scaling involves adding more resources to the same server for scalability. This innate nature of SQL makes it unsuitable for databases with a distributed architecture.

Also Read: 13 Key Differences Between MySQL vs MsSQL: Which Database To Choose in 2024?

Key Differences Between MongoDB and SQL: A Detailed Comparison

It’s time to explore the differences between SQL and MongoDB and see what makes them so unique. This should help developers decide which database is ideal for future projects.

Data Structure

There is a stark difference between MongoDB and SQL in terms of data structure. MongoDB is the opposite of SQL, built as an alternative and categorized as NoSQL.


Data is stored in MongoDB as collections, each with a different structure. Collections can contain several different documents, each structured differently and using different data types. There is no static schema; the schema is dynamic and free-flowing. Documents are not limited to having the same fields inside, allowing diverse collections with various data to exist without issue in the document database.

MongoDB’s schema-less, unstructured characteristic makes it the perfect choice for applications needing database flexibility.


An SQL database is a structured data collection stored in tables, with each row representing a record and each column denoting a data field. Such a structure is highly organized and optimal for powerful and complex SQL querying.

The database schema is rigid and resistant to change. The data structure must be properly defined before data can be input into the database. Data must be in every row for each column, or the data field must be explicitly defined to stay null.

SQL’s organized structure and resistance to change make it ideal for databases that do not undergo frequent change and favor consistency.

Query Language

The query language is the next big difference to cover in the MongoDB vs SQL comparison. Both utilize unique languages and features for querying and data storage.


NoSQL databases like MongoDB use a querying structure resembling JSON documents. The querying language, meanwhile, is MongoDB query language: MQL. MQL offers similar functionality as SQL. Joins are supported differently, however, and remain distinct from SQL.

Querying in MongoDB is done through a rich feature library. Some notable features are indexing, ad hoc queries, and real-time aggregation. These querying features allow for dynamic database manipulation and streamlined application performance.


SQL databases use the SQL querying language for data retrieval and manipulation. SQL is especially effective for queries that involve multiple tables that need to be joined on specific data points.

As the standard querying language for decades, SQL has amassed a significant following and is well-known to developers. When comparing MongoDB vs SQL, SQL may be preferred simply because many developers are intimately familiar with its workings.


Scalability is a fairly even race for MongoDB vs SQL. Both languages scale differently, each with ups and downs that must be considered.


MongoDB is optimized for horizontal scaling, allowing organizations to add more machines to the network for higher storage and performance. This sharding feature of MongoDB is also very useful for preventing data loss, as databases can be replicated and stored across multiple machines.

Having many machines acting as hosts also keeps things running in case one crashes or critically fails. MongoDB is more scalable than SQL for businesses with limited resources.


SQL typically scales vertically, with the same hardware undergoing upgrades for growing needs. This can get quite expensive long-term, as more advanced technology and expensive equipment are needed to sustain the database. There is also more risk of data loss, as the machine breaking down can bring things to a grinding halt. SQL’s transaction use and data integrity features prevent most damage, but it is still risky.


Comparing MongoDB vs SQL transactions is integral to determining which database is best for you.


MongoDB is ACID-compliant at the document level and has even introduced multi-document transaction capability. However, these features are still quite young and cannot handle complex workloads the same way SQL can.


SQL provides incredibly advanced, robust transaction capability. SQL transactions prioritize data integrity and are built for consistency. No matter what happens, data is always recoverable and can be rolled back thanks to the ACID-compliant mechanism.

Use Cases

MongoDB and SQL both have strengths ideal for unique use cases. Some of the notable use cases are listed below:


Being a non-relational database makes MongoDb more flexible than other databases. It is well-suited for rapidly evolving data models, large-scale applications requiring a database that can scale easily with minimal downtime, and data storage with a varied structure.

Typical use cases include big data applications, content management, and mobile and social media apps. MongoDB is used by notable businesses like Forbes for database management.


SQL is ideal for applications requiring complex transactions, strict data integrity, and standardized data structures. Common use cases include financial systems, inventory management, and other systems where data consistency is critical. Netflix is a notable user of SQL to watch out for.

Here is a useful chart summarizing the discussion surrounding MongoDB vs SQL so far:

Key Differences Between MongoDB and SQL: A Detailed Comparison

MongoDB vs SQL: Choosing a Database for Your Next Project

The choice of MongoDB vs SQL is entirely dependent on project parameters and database requirements. For developers conflicted between the two for their next project, they can rest easy in the knowledge that both databases are equally robust and good at what they do. The choice boils down to whether you prefer using one over the other.

MongoDB is quite formidable for small businesses, as horizontal scaling helps them save costs and allows them to adapt strategically during the shaky initial years. Applications that prioritize dynamic activity and frequently utilize unstructured data like videos are good candidates for a MongoDB database.

SQL is good for pretty much any database-related activity. Relational databases hold a lot of trust from the programming world, and organizations wanting tried-and-tested reliability will prefer SQL. SQL’s robust security and data integrity make it ideal for handling sensitive data compared to MongoDB.

Let us know who won the MongoDB vs SQL for you, and why!


This concludes our exploration of the MongoDB vs SQL question. Comparing their strengths and weaknesses highlights how different they are despite their similarities. Databases are a miraculous technology behind many of the things we know and love today. But it isn’t just databases that support this online world.

In all these software-related discussions, the hardware facilitating and making it all possible is rarely given due attention. Whether you use MongoDB or SQL, both are useless without a robust server hosting them. We’ve gone over the importance of server maintenance a few times in the article, and it cannot be stressed enough how important it is to always remember the hardware side of things.

RedSwitches Bare Metal Servers are some of the most sturdy and robust homes for your precious databases. Whether you’re using MongoDB, SQL, or any other iteration, RedSwitches can help you maximize your application’s potential.


Q. What is the basic difference between MongoDB and SQL in data storage?

MongoDB stores data in flexible, JSON-like documents, meaning fields can vary from document to document, and data structure can be changed over time. SQL databases store data in tables and rows, which require a predefined schema.

Q. How does querying differ in MongoDB vs SQL databases?

MongoDB uses a document-based query language that is quite expressive, yet different from SQL. SQL uses a structured query language that is highly powerful for complex queries involving multiple tables.

Q. Which is better for handling big data, MongoDB vs SQL?

MongoDB is often preferred for big data applications due to its high write loads, horizontal scaling capabilities, and flexible schema.

Q. What are the transaction capabilities of MongoDB vs SQL?

SQL databases provide robust transaction capabilities with strong consistency. MongoDB has introduced multi-document transactions in its recent versions, but SQL is generally more mature in handling complex transactions.

Q. How do MongoDB vs SQL handle scalability?

MongoDB is designed for easy horizontal scalability through sharding. SQL databases typically scale vertically, although many modern SQL databases also support horizontal scaling.

Q. What type of integrity constraints are available in MongoDB vs SQL?

SQL databases offer various integrity constraints, such as foreign keys, unique constraints, and check constraints. MongoDB does not natively enforce these types of constraints.

Q. How do MongoDB and SQL databases manage indexes?

Both MongoDB and SQL databases use indexes to optimize query performance. However, MongoDB automatically indexes the _id field, while SQL requires explicit index creation.

Q. Which database offers more flexible schema options, MongoDB vs SQL?

MongoDB offers a more flexible schema that can evolve with your application’s needs, making it ideal for applications with rapidly changing data.

Q. What are the replication features like for SQL and MongoDB data?

Both MongoDB and SQL databases offer replication features. MongoDB’s built-in replication is more straightforward to set up and manage using the concept of replica sets.

Q. How do backups work in MongoDB vs SQL?

Both MongoDB and SQL databases support full and incremental backups, but the tools and processes might differ. MongoDB offers tools like Mongodump and mongorestore.

Q. What are the security features like in MongoDB vs SQL?

Both database systems offer robust security features, including authentication, authorization, and encryption. SQL databases generally have more mature role-based security models.

Q. How do MongoDB vs SQL handle data recovery?

MongoDB and SQL databases provide mechanisms for data recovery in case of data loss. SQL databases typically use transaction logs, while MongoDB uses oplogs in replica sets.

Q. Which is more cost-effective for startups, MongoDB vs SQL?

MongoDB can be more cost-effective for startups due to its flexibility and lower maintenance requirements for rapidly evolving data models.

Q. What programming languages offer libraries for MongoDB vs SQL?

A wide range of programming languages, including Python, Java, JavaScript, C#, PHP, and more, support MongoDB and SQL databases.

Q. When should I use MongoDB and SQL?

MongoDB is often used for mobile apps, content management, real-time analytics, and applications with unstructured data. SQL is preferred for applications requiring complex queries and high transactional integrity.

Q. What are the learning curves like for MongoDB vs SQL?

SQL might have a steeper learning curve due to its complex query language, while MongoDB’s JSON-like document structure can be more intuitive for developers familiar with JavaScript.

Q. How do MongoDB vs SQL handle concurrent data access?

SQL databases handle concurrency with locking mechanisms and transaction isolation. In recent versions, MongoDB handles concurrency with a locking system at the document level.

Q. Can MongoDB and SQL be used together?

Yes, many applications use MongoDB alongside SQL databases to leverage the strengths of both types of storage.

Q. What are the data validation options in MongoDB vs SQL?

SQL databases enforce data validation using constraints at the schema level. MongoDB supports data validation rules that you can define at the collection level.

Q. What are the performance differences between SQL and MongoDB when handling large datasets?

MongoDB may perform better with large datasets that don’t require complex joins due to its efficient handling of big data and horizontal scalability.

Q. What are the maintenance considerations for MongoDB vs SQL?

MongoDB typically requires less maintenance for scaling and schema changes, while SQL databases might require more attention to performance

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