A Database is a logical collection of data that can be used to store and manipulate data from a computer system electronically. 

Every organization needs a Database for storing transactional data, company data, metadata, performing analysis, and much more. 

Database technologies have evolved over the years and provide a huge range of features to choose from. Depending on the use case, a Solution Architect or a Developer chooses a Database architecture.

Two very popular Database technologies that have their own demand are MongoDB and MySQL.

MongoDB is the popular NoSQL Database and MySQL is the popular SQL Database.

Are you confused between MongoDB vs MySQL decision? If yes, then this article highlights the key differences between MongoDB and MySQL to help you choose the right Database for your business.

Table of Contents

What are SQL and NoSQL Databases?

MongoDB vs MySQL - SQL vs NoSQL Differences
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Before proceeding forward, let’s take a glance at the basics of SQL and NoSQL Databases:

SQL (Relational) Databases

SQL(Structured Query Language) is used in Relational Databases (SQL Databases) to create, update and delete database objects. It uses tables to define relationships while following strict ACID(Atomic, Consistency, Isolation & Durability) properties. 

NoSQL (Non-Relational) Database

NoSQL Databases do not follow a structured data format. These are non-tabular databases that can easily scale with large volumes of data and high user loads.

Want to explore the key differences between NoSQL and SQL Databases? Read SQL vs NoSQL Databases: 5 Critical Differences to gain a better understanding.

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Hevo Data, a No-code Data Pipeline helps to integrate data from MongoDB and MySQL (among 100+ sources) to the destination/ Data Warehouse of your choice.

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Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.

It also allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.

What is MongoDB?

MongoDB vs MySQL: MongoDB logo
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MongoDB is an Open-Source, NoSQL Database. It stores data in JSON-like documents. It is created, maintained, and managed by MongoDB.Inc under the SSPL(Server Side Public License) license.

MongoDB is also a Distributed Database which makes it highly scalable, available, and geographically distributable. It is available for free to learn and develop small applications.

MongoDB can be connected using 10+ programming language drivers(C++, Java, Python, JavaScript, etc) and also provides end-to-end security and management tools for automation, monitoring, and backup.

Explore more about MongoDB in the official Documentation.

What is MySQL?

MongoDB vs MySQL - MySQL Logo
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MySQL is an Open-Source, multithreaded, multi-user, Relational Database. It is a trademark of Oracle Corporation and is available for free if you want to develop small-scale applications.

MySQL is scalable, reliable, and easy to use. You can run it on a laptop or a desktop. It uses a basic Client-Server Model to assist users in managing Relational Databases, or data stored in rows and columns across tables.

MySQL client programs can be written in C, C++, Eiffel, Java, Perl, PHP, Python, Ruby, and Tcl.

Want to explore more about MySQL? Refer to the official website.

Key Differences: MongoDB vs MySQL

Here’s a quick comparison table before we dive deep into the differences between MongoDB and MySQL:

Relational/ Non-RelationalNon-Relational DatabaseRelational Database
Data TypeUnstructured and/or Structured DataStructured Data
Data RepresentationMongoDB represents data as JSON documentsMySQL represents data in tables and rows
Defining SchemaNo need to define Schema in MongoDBYou need to define tables and columns in MySQL
JOIN OperationsNot SupportedMySQL supports JOIN operations
Query LanguageJavaScriptSQL
PriorityIf you have multiple cloud-based services When the priority is security
Scalability Easily ScalableScalability is limited

MongoDB vs MySQL: Data Storage


  • MongoDB doesn’t have a rigid schema to store the data. 
  • It stores data in Collections and Documents rather than tables with rows and columns.
  • The Collections are made up of a number of documents, each of which contains the fundamental units of data in the form of key-value pairs.


  • MySQL stores data in tables that are made up of rows and columns.
  • All data relationships follow a strict logical structure.
  • MySQL provides different storage engines to store the various data types. Refer to MySQL Storage Engines to learn more.

MongoDB vs MySQL: Architecture


  • MongoDB’s Nexus Architecture design philosophy combines the capabilities of a Relational Database.
  • This architecture also tends to the needs of modern-day applications by providing flexible schema, high scalability, and highly available on-globe deployment.


  • MySQL has a client-server architecture that has storage optimized for multi-threading and high performance.
  • MySQL has documented some measures to optimize the performance of the server. You can read these here.

MongoDB vs MySQL: Query Language


  • MongoDB uses MongoDB Query Language (MQL), which is rich and expressive. 
  • It supports CRUD operations(Create, Read, Update, Delete), data Aggregation, text search, and Geospatial queries. Learn more about MQL here.


  • MySQL uses Structured Query Language(SQL) like any other Relational Database.
  • SQL has
    • Data Definition Language (CREATE, ALTER, and DROP tables)
    • Data Manipulation Language (INSERT, UPDATE and DELETE rows)
    • Data Transaction Language (COMMIT, ROLLBACK, etc)
    • Data Control Language (GRANT and REVOKE) command.
  • It also supports joins to fetch data from multiple tables.

MongoDB vs MySQL: Schema Flexibility


  • MongoDB is amazing for modern-day applications as it offers a flexible schema design that allows you to meet the ever-changing conditions of Big Data applications.
  • With MongoDB, you can easily store and combine any type of data and dynamically modify schema without experiencing application downtime.


  • MySQL is amazing if you have a predefined and fixed schema. It allows you to maintain consistency in data.
  • It is quite difficult to use MySQL if the needs of your organization require a dynamic schema.

MongoDB vs MySQL: Performance


  • When it comes to the performance of MongoDB on unstructured data it is relatively very fast when compared to MySQL because of its document-based data storage.
  •  It Performs better than MySQL when working with Objects due to its JSON type Object storage.


  • MySQL performs great in transactional operations.
  • However, when the data volume gets huge you can observe slow performance. The reason is that the tables are stored in a normalized format and to fetch or change data, it needs to go through lots of tables which increases the load on the server and affects the performance.

MongoDB vs MySQL: Security


  • MongoDB provides security features like authentication, access control (user,role-based access control), and encryption(TLS/SSL) for sensitive data.
  • Security features also depend on the pricing tier.


  • MySQL offers normal encryption and normal security policies for the Standard Edition.
  • But for the other two editions, MySQL offers authentication, TDE, encryption, masking, firewall, and audit.

Similarities between MongoDB and MySQL

Reading the above comparison of MongoDB vs MySQL, you might have observed that MongoDB and MySQL have distinct features that are best suited to specific use cases.

However, there are many similarities between these two systems. Some of these are: 

  • Both are Database Management Systems (DBMS) at their core. 
  • Both MySQL and MongoDB support the following programming languages via drivers (or interfaces): NodeJs, Java, Python, and Server-side PHP.
  • As part of the foundation of web applications, they store data on a computer system or network.
  • To function properly, web applications require a data foundation in the form of database services. MongoDB and MySQL, for example, enable enterprises to share, alter, and deploy cloud-native applications.
  • The code for both systems is open-source, which means that anybody can use and share it.

Pros & Cons of MongoDB

Some of the benefits of MongoDB are:

  • The schema of MongoDB isn’t predefined. Its dynamic schematic architecture allows it to operate with unstructured data.
  • As MongoDB is schemaless, expansion plans are flexible, and extra systems and RAM can be readily added.
  • MongoDB supports joins in queries, multi-Documents ACID transactions, and supports embedded & reference relationships between Documents.
  • It has a very powerful and expressive query language that allows you to search, sort, and filter using any field.
  • Its rich JSON-like Documents allow flexible and dynamic schemas.
  • MongoDB Atlas provides a Multi-Cloud Database service on AWS, Google Cloud, and Azure for the modern Cloud era.
  • It provides on-demand scaling, resource optimization tools, and real-time insights intoon your Database performance.
  • It is a fault-tolerant Distributed Database with a lot of data recovery options.
  • It uses a document-based query language that is nearly as strong as SQL to allow dynamic queries on documents. Check out how you can Conduct MongoDB Query Performance Analysis easily.

Some of the limitations of MongoDB are:

  • The transactions using MongoDB are complex.
  • When it comes to ACID properties, MongoDB is not as strong as compared to many other RDBMS systems.
  • MongoDB doesn’t support any Stored Procedures or functions and fails when it comes to implementing any business logic at the Database level that you can easily achieve in other RDBMS systems.

Pros & Cons of MySQL

Some of the benefits of MySQL are:

  • MySQL is simple and easy to use and anyone with basic SQL knowledge can work with MySQL.
  • MySQL allows COMMIT and ROLLBACK for transactions. 
  • It has built-in support for optimizing, repairing, and checking tables.
  • MySQL has a large user- community.
  • MySQL is extremely versatile since it supports a wide variety of embedded applications.
  • In MySQL, all passwords are encrypted and it also provides host-based authentication and is very secure.
  • Its multi-threaded design with kernel threads allows you to work on multiple CPUs and implements in-memory hash tables to optimize performance.
  • Supports both transactional and non-transactional storage.

Some of the limitations of MySQL are:

  • If sometimes a server crash happens, it can corrupt the system catalog.
  • Transactions that are related to the catalog are not ACID compliant.
  • Most of the MySQL tables used for the procedure or trigger are pre-locked.

Applications of MongoDB

Thousands of enterprises leverage MongoDB for data storage or as a database service for their applications all around the world. It’s ideal for: 

  • Legacy organizations looking to upgrade their big data infrastructure.
  • High-query sites and applications, such as analytics software. 
  • Content management systems (CMS) such as WordPress.
  • The MEAN (MongoDB, ExpressJS, AngularJS, and NodeJS) technological stack for web development.

Use Cases of MongoDB

MongoDB is used by companies such as IBM, Oracle, Zendesk, Sony, Intercom, and others. Here are a few of MongoDB’s most well-known use cases:

  • Aadhar: Aadhar is India’s Unique Identification Project and the world’s largest biometric database. MongoDB is one of the databases it utilizes to store more than 1.2 billion people’s biometric and demographic data.
  • eBay: It is a B2C and C2C eCommerce corporation that employs MongoDB for a variety of projects including search recommendations, cloud management, and metadata storage.

Applications of MySQL

MySQL has been around for nearly two decades and has provided enterprises all around the world with the simplicity of storing enormous amounts of data. Here are some of MySQL’s applications:

  • Sites with a high volume of visitors, such as e-commerce or social sites.
  • Sites that need stringent security standards such as government-based and compliance-heavy sectors.

Use Cases of MySQL

Airbnb, NASA, YouTube, Netflix, Pinterest, Drupal, Joomla, and other notable enterprises use MySQL. Here are a few of the most common MySQL use cases:

  • Wikipedia: Wikipedia, a free web encyclopedia runs on MySQL to keep up with the rising user base. MySQL keeps the page up to date, handles content and visitors, and allows for thousands of additions and modifications.
  • Twitter: T-bird is being used by Twitter to shift away from temporal sharding for tweet storage and toward a more distributed technique. T-bird is based on Gizzard, which employs MySQL.

Frequently Asked Questions (FAQs)

Is MongoDB easier than MySQL?

MongoDB’s schemaless nature makes it simple to create and upgrade applications over time, without the need for difficult and costly schema migration processes as you would with a relational database.

In comparison to MySQL, there will be a significant performance increase. 

MySQL, on the other hand, has a larger online community since it has been around longer. Hence, it will be easier to debug your code or get answers easily.

Should I Use MySQL or MongoDB?

Use MySQL when:

  • You have structured data and need a traditional relational database.
  • You’re just getting started and don’t expect your database to grow much,
  • Data security is a high priority.

Use MongoDB when:

  • Your data is unstructured and dynamic, or if you can’t pre-define your schema.
  • You want high data availability with automated, quick, and immediate data recovery.
  • You plan to scale up in the future. MongoDB includes a built-in sharding option.

Can I Set Up a MongoDB MySQL Integration?

There are many ways of loading data from MongoDB to MySQL. You can use a manual ETL Process to Set Up MongoDB MySQL Integration using the Mongoexport tool.

In addition, you can use an automated Data Pipeline platform like Hevo that can move your data between MongoDB and MySQL very quickly without writing a single line of code. It is simple, hassle-free, and reliable.

Find out more about Connecting MongoDB to MySQL using two 2 Easy Methods.


We hope that the comparison of MongoDB vs MySQL has given you a good understanding of the two Database Management Systems. 

Both MongoDB and MySQL are great in their own way. While it may seem that MongoDB Databases save a lot of time in Database definition initially in the development process, the fact remains that a well-defined Database schema with MySQL can give a sizable performance advantage in some cases.

Eventually, MySQL is used in specific scenarios (fixed schema constraints). 

MongoDB is used if your application deals largely with Objects and if your requirement is schema flexibility. 

Also, you can split the responsibilities between MySQL and MongoDB giving you the freedom to exploit both of their features in a hybrid format.

Whether you select one or choose a hybrid combination of the two Databases, the success of your application will depend on having access to data transfer tools that can work between these systems and external data sources. 

Having a No-code Data Pipeline like Hevo can help you integrate data from MongoDB & MySQL and load them into a Data Warehouse of your choice. 

Thus, making data migration hassle-free and seamless.

Want to take Hevo for a spin?

Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Check out the pricing details here.

Now that you are aware of the differences between MongoDB and MySQL and also have an understanding of why how data replication can help, can read these blogs on replicating data from both the databases:

Do let us know your thoughts about this blog and if you know any other differences in the comments 😊

Former Research Analyst, Hevo Data

Easha is a programming enthusiast having experience in automated test script creation, regression testing, and integration projects like Thyrocare Integration. She is keenly interested in creating technical articles related to data science and integration.

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