PostgreSQL vs MongoDB: Choosing the Right Database for Your Data Projects

If you’re planning to start a new digital application, you will likely need to collect and store some data. Where and how to store that data is one of the most important decisions you will have to make, for the whole software will rely on that data to work. 

Traditionally, data has been stored in tables with rows and columns connected through certain relationships (i.e., common columns). Databases that store relational data are called relational databases, or SQL databases, for most, if not all, rely on SQL for all kinds of operations.  

However, in recent decades, new database management systems have evolved to address the rapid increase in the volume and variety of data that is created every second. The so-called NoSQL databases (Not only SQL) propose new schemas and strategies to effectively collect data based on specific use cases, while still relying to some extent on SQL.

In this article, we will analyze two popular database management systems: PostgreSQL vs MongoDB. The former is one of the most popular SQL databases, whereas the latter is the most famous NoSQL database.

We will cover the key features and strengths of both databases, their most compelling use cases, and good things to remember in case you’re considering migrating your data to one of these databases. 

If you’re looking to get hands-on and try out each method, be sure to check out our courses, Creating PostgreSQL Databases and Introduction to MongoDB in Python.

TL;DR: PostgreSQL vs MongoDB

Choosing the right database is crucial for any digital application. PostgreSQL, a powerful SQL-based relational database, excels in structured data scenarios requiring consistency, complex joins, and ACID transactions. MongoDB, a flexible NoSQL document database using BSON, is ideal for handling dynamic, unstructured data, offering greater scalability through horizontal sharding. Selecting between PostgreSQL and MongoDB ultimately depends on your project's data structure, scalability needs, and consistency requirements.

PostgreSQL vs MongoDB Data Model Architectures

Let’s analyze the main differences between PostgreSQL and MongoDB in terms of data model architecture.

Relational vs document-based structures

Relational database management systems, like PostgreSQL, organize data into tables, where each table consists of rows and columns. These tables can be linked through keys, allowing for complex data relationships through SQL joins and efficient querying.

Relational database. Source: DataCamp

Thanks to its simplicity, unique efficiency, and strong consistency, relational databases have been extremely successful and widely adopted in the last decades.

However, they might not be the best option when you work with unstructured data that doesn’t fit neatly into a tabular format (e.g., social media posts, sensor data) or when scalability is a must; that is, when your application needs to scale horizontally across numerous servers.

Here is where NoSQL databases join the scene. Unlike their relational counterparts, NoSQL databases can handle unstructured or semi-structured data without the constraints of a fixed schema. 

Particularly, MongoDB is a so-called document database, a type of NoSQL database that stores data in collections of JSON-like documents using a format called BSON (Binary JSON). In simple terms, documents are similar to JSON key-value objects, with additional capabilities to store and manipulate data provided by BSON. 

How MongoDB stores data as BSON. Source: DataCamp 

The flexible nature of BSON enables the creation of dynamic document schemas whose structure and data can vary on the fly. We will see more on this in the next subsection.

Schema evolution patterns

Relational databases like PostgreSQL provide robust schemas that are hard to alter once you create your tables. This is a great feature if the data you’re working with has a tabular nature and always arrives in your application in the same format. However, if you deal with semi-structured or unstructured data whose content can vary from one piece to another, such as social media posts or sensor data, you may need some level of flexibility to digest your data. 

In contrast with PostgreSQL, MongoDB comes with so-called schema-less designs, which offer exceptional flexibility for handling diverse and evolving data. MongoDB’s schema-less design organizes data into documents and collections:

• Documents are the basic unit of data, consisting of BSON key-value pairs. Documents can contain strings, numbers, dates, arrays, and even other embedded documents. This allows for sophisticated data models representing complex relationships within a single document, which aligns with how objects are structured in most programming languages.
• Collections group related documents, similar to a table, but in a more flexible fashion. Collections don't enforce a schema, so that documents can have different structures and fields. Collections exist within MongoDB databases, and each database can contain multiple collections.

Unlike traditional relational databases, where adding a new field requires altering the entire table structure, MongoDB allows documents within the same collection to have completely different fields and structures. This eliminates the need for rigid schemas and makes it easier to store varied data without forcing uniformity.

PostgreSQL vs MongoDB Query Languages

No matter the database you’re using, whether PostgreSQL or MongoDB, you will need to learn the language to communicate and manage your databases. In PostgreSQL, this is SQL, an extremely popular, standardized, and easy-to-use programming language. You can get started with SQL in our Introduction to SQL course. 

By contrast, MongoDB primarily relies on its own MongoDB Query Language (MQL) for interacting with the database. However, it’s also possible to use other popular programming languages, such as Python, C, and Java, to connect and interact with MongoDB databases.

Let’s analyze the main differences between SQL and MQL.

SQL vs MQL capabilities

One of the greatest advantages of PostgreSQL compared to MongoDB is the use of SQL. SQL (stands for “Structured Query Language) is an essential language for building and maintaining relational databases. 

It’s a simple, domain-specific programming language that is used by nearly all relational database systems, such as MySQL, SQL Server, SQLite, and, of course, PostgreSQL.

SQL is extremely powerful, allowing you to perform all kinds of data operations, from data retrieval and aggregation to data cleaning and joining operations. However, when it comes to PostgreSQL, the possibilities are even greater, for PostgreSQL comes with a set of advanced capabilities for complex analysis, including:

• Functions and procedures: PostgreSQL supports the creation of functions and stored procedures, which can be written in various programming languages, enhancing the database's ability to handle complex operations.
• Full-text search: PostgreSQL offers robust full-text search capabilities, enabling efficient search operations on textual data.
• JSON support: Extensive support for JSON data types allows PostgreSQL to handle semi-structured data effectively, somehow bridging the gap between relational and document-oriented databases.

By contrast, although MongoDB doesn’t rely on SQL, it has powerful querying, indexing, and aggregation capabilities, as well as other functions to insert, update, or delete information in document databases. Hence, if you are familiar with JSON or Python dictionaries, it won’t take you much to become familiar with MQL syntax. 

MQL is specifically designed to handle the dynamic nature of documents. As a result, it’s a much more flexible language compared to SQL. Also, as documents can embed other documents inside, in MongoDB, there is no need to use complex and inefficient join operations, which is often the case in relational databases. 

Indexing strategies

Indexes are database objects that improve the speed of data retrieval operations. They serve as pointers to quickly locate rows in a table, enhancing the performance of queries. 

PostgreSQL comes with strong indexing capabilities, supporting multiple index types, including:

• B-Tree Index: The default and most commonly used index type
• Hash Index: For fast equality searches
• GIN Index: For indexing JSON, arrays, and full-text search
• GiST Index: For complex data types like geometric or fuzzy search
• BRIN Index: For large, naturally ordered data (like time-series logs)
• Expression Index: Index based on a function or expression
• Partial Index: Index only a subset of rows (useful for filtering)

MongoDB also supports indexing to find documents in collections without having to scan all the documents in a collection. However, its capabilities are limited, lacking the variety and sophistication of indexing available in relational databases like PostgreSQL. This includes:

• Single field Index: Single field indexes collect and sort data from a single field in each document in a collection.
• Compound Index: Compound indexes collect and sort data from two or more fields in each document in a collection.
• Multikey Index: Multikey indexes collect and sort data stored in arrays.
• Text Index: Text indexes support text search queries on fields containing string content.
• Hashed Index: Hashed indexes support hashed sharding. Hashed indexes index the hash of the value of a field.

Using indexes in your SQL and NoSQL databases is key to increasing performance and reducing query times, especially on large datasets. 

However, remember that adding an index can also have a negative performance impact for write operations. For tables or collections with a high write-to-read ratio, indexes are expensive because each insert must also update any indexes.

Performance and Scalability in MongoDB vs PostgreSQL

MongoDB and PostgreSQL are great database tools, but there is no point in choosing one over the other based on their properties. Instead, you should always make your choice considering the particularities of your use case. Having this in mind, let’s analyze the differences in performance and scalability of MongoDB and PostgreSQL.

Transaction processing

Like other popular relational database systems, PostgreSQL ensures high levels of data integrity and quality through its robust typing system and support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. 

ACID transactions are a set of properties that guarantee the reliable processing of database transactions. They ensure that data remains accurate and secure even in the face of errors, crashes, or concurrent access. These properties are vital for maintaining data quality in any project.

While ACID transactions have long been the gold standard for ensuring data integrity in relational databases, NoSQL databases like MongoDB often prioritize flexibility and scalability over strict transactional consistency. This results in relaxing some of the ACID properties found in relational databases. 

For example, some MongoDB databases prioritize eventual consistency over immediate consistency, meaning that changes might not be reflected across all nodes instantly. The trade-off in terms of consistency, availability, and performance allows for better performance and scalability but requires careful consideration when designing applications that rely on strict data consistency.

Scalability patterns

Scaling involves the use of several techniques to address increases in data volume and traffic. In simple terms, we can do this through vertical scaling and horizontal scaling. The former involves improving and adding resources to the same server to handle increased load. The latter is achieved by adding more servers or nodes to a distributed system, which then helps increase capacity. 

PostgreSQL primarily relies on vertical scaling to increase capacity. However, unlike other popular SQL databases, PostgreSQL also permits horizontal scaling through partitioning, yet it requires complex configurations. You can learn all the technicalities of partitioning in PostgreSQL in our Improving Query Performance in PostgreSQL Course.

By contrast, in NoSQL systems like MongoDB, the go-to strategy to handle growing data traffic and volume is horizontal scaling.  MongoDB is designed to leverage sharding, a technique that involves dividing a database into smaller, more manageable pieces called "shards” and distributing them across multiple servers, enabling the system to handle large datasets and high volumes of traffic.

Check out our Sharding vs Partitioning article to know everything about these popular scaling techniques.

MongoDB vs PostgreSQL: Use Case Analysis

Now that we know the key features and strengths of PostgreSQL and MongoDB, it’s time to analyze the scenarios where they are more useful.

Ideal PostgreSQL scenarios

PostgreSQL is a great choice when your project will require:

• Strong consistency: Ensuring all users see the same data simultaneously.
• Complex queries: Joining data from multiple tables to gain insights.
• Advanced analytics: If you need to perform complex calculations or data transformations within the database, PostgreSQL's extensibility is invaluable.
• Scalability: For projects expected to grow significantly, PostgreSQL's ability to handle large datasets through vertical scaling or partitioning is a major advantage.
• ACID compliance: Guaranteeing reliable transaction processing for critical applications.

To give some compelling examples, PostgreSQL is a sound idea in:

• Financial transactions: Accuracy and consistency are crucial when transferring money or processing payments.
• Inventory systems: Ensuring stock levels are updated accurately is vital to avoid overselling or discrepancies.
• Order processing: To satisfy customers in e-commerce, orders need to be processed correctly and consistently.

Optimal MongoDB implementations

MongoDB works best in cases when it's useful to have flexible data structures that can dynamically adapt to new information and schemas, when scalability and performance are important, and for unstructured data. MongoDB’s schema-less design, paired with horizontal scaling, makes it great for use cases like:

• Social media feeds: Since much of the incoming data is unstructured and unpredictable, data consistency is less critical, and temporary inconsistencies in posts or likes are acceptable as long as the system remains responsive.
• Content delivery networks: Serving content to users with minimal latency is prioritized over consistency.

Hybrid architectures

There may be use cases where you need to combine the strengths of MongoDB and PostgreSQL to advance a comprehensive solution for diverse data requirements. Creating hybrid architectures combining SQL and NoSQL databases is possible, and a great idea if you need to combine strong consistency on your transactions with some degree of flexibility. 

For example, you can use MongoDB for fast writes in real-time analytics while using PostgreSQL for complex relational operations and storing structured data. However, there isn’t currently such a way to “merge” the functionalities of both databases. You will be just using two different databases at the same time, which will increase the complexity of your system, requiring careful analysis before implementing it. 

Migration Considerations

If you are planning to migrate data from MongoDB to PostgreSQL or the other way round, you will have to consider several elements:

Database schemas

If you’re migrating from MongoDB to PostgreSQL, before migrating the data, you will have to create a table or multiple tables with a predefined schema, that is, a list of columns with their assigned data types and potential constraints. If you want to move your data into Mongo, you will need to define how the data will be incorporated into the documents. Despite the flexible nature of documents, some data modeling will be essential to place the existing data.

Potential data transformations

The data you want to export may not have the desired format. If that’s the case, before migrating, you will have to establish some sort of data pipeline to transform the data into the right format before inserting it into the PostgreSQL or MongoDB database.  

How to migrate the data

There are several ways to do a migration process. You can do it manually for basic export/import steps, but it can be a time-consuming, complex, and prone-to-error process, especially if the database you want to migrate contains a lot of data. That's why choosing an ELT to get the job done may be a better solution. There are many available solutions out there, both from migrating from PostgreSQL to Mondo and vice versa.

Performance optimization

Considering techniques to increase performance during migration is crucial, especially in complex migration processes. Again, depending on the source and the destination, PostgreSQL and MongoDB offer specific techniques and strategies to speed up the process, including connection pooling, partitioning, shard key selection, and covered queries.

Security and Compliance

Protecting your database deployment is critical to ensuring data integrity, availability, and compliance. Both MongoDB and PostgreSQL offer diverse methods to secure your databases. In this section, we cover some of the most common techniques.

Authentication mechanisms

PostgreSQL comes with a range of authentication methods, which can be managed effectively through roles. Roles are entities that can own database objects and have database privileges. Roles can be used for authentication purposes, permission management, and defining user access levels. In PostgreSQL, a role can function as a user or as a group of users.

You can also create roles in MongoDB to prevent unauthorized access. The role-based control follows the principle of least privilege to minimize security risks. MongoDB comes with several authentication mechanisms, with Salted Challenge Response Authentication Mechanism (SCRAM) being the default mechanism for secure password-based authentication.

Encryption practices

MongoDB offers a good number of encryption practices to secure your data, including:

• TLS/SSL encryption: Use TLS/SSL to encrypt data in transit, ensuring secure communication between clients and the database.
• IP whitelisting and VPNs: Restrict access to your MongoDB instances by allowing only trusted IP addresses. For additional security, use VPNs to create secure tunnels for accessing production databases.
• Encryption at rest: You can enable encryption at rest using MongoDB’s native support in the WiredTiger storage engine. This protects sensitive data stored on disk.
• Client-side field-level encryption: For high-security requirements, use client-side field-level encryption to encrypt specific fields before they are sent to the database. This ensures that even database administrators cannot access sensitive information.

As for PostgreSQL, it also offers encryption at several levels and provides flexibility in protecting data from disclosure due to database server theft, unscrupulous administrators, and insecure networks. Some options include:

• Password encryption: Database user passwords can be stored as hashes, so the administrator cannot determine the actual password assigned to the user.
• Encrypting data across a network: SSL connections encrypt all data sent across the network: the password, the queries, and the data returned. 
• Data partition encryption: Storage encryption can be performed at the file system level or the partition level.
• SSL host authentication: Both the client and server can provide SSL certificates to each other.

Community and Ecosystem

Both MongoDB and PostgreSQL are highly popular tools, with growing communities of users and thriving ecosystems. Let’s have a look at them.

Extension capabilities

PostgreSQL extensions in PostgreSQL are add-on modules that expand the capabilities of the database. They are like packages in Python or R. Over the last years, a rich ecosystem of extensions has sprung up to boost the potential of PostgreSQL in all kinds of scenarios, disciplines, and use cases. Most of them can be found in the PostgreSQL Extension Network.

By contrast, MongoDB comes with a more limited set of extensions and is mostly restricted to enabling the use of MongoDB with other technologies and frameworks, such as IDEs like Visual Studio Code, and cloud systems like Google Cloud. However, other third-party packages may be available for the different programming languages you normally use to manage MongoDB.

Adoption trends

Both PostgreSQL and MongoDB are living golden times, as depicted in the following image with the trends of database engines based on data from DB-Engines.

Source: db-engines

While relational database management systems are still dominant (including PostgreSQL, which ranks 4th),  the graph also reveals a notable shift in recent years, with NoSQL databases like MongoDB and Redis experiencing significant growth. This upward trajectory reflects the increasing adoption of these flexible and scalable solutions for managing unstructured data and accommodating high-traffic applications.

PostgreSQL vs MongoDB Comparison Table

Here is a great table summarizing the differences between MongoDB databases and PostgreSQL:

Conclusion

MongoDB and PostgreSQL are two of the most popular databases out there. They are great examples of SQL and NoSQL databases, and by comparing them, we can shed some light on the differences, strengths, and varying use cases where these databases may be useful.

There’s a lot to learn about SQL and NoSQL databases. At DataCamp, we want to help you with the most comprehensive and updated courses and tutorials. Check out some of our dedicated database material here:

• Introduction to NoSQL Course
• Introduction to MongoDB in Python 
• NoSQL Concepts
• Improving Query Performance in PostgreSQL 
• PostgreSQL Basics Cheat Sheet 
• SQLite vs PostgreSQL: A Detailed Comparison 
• Top 25 MongoDB Interview Questions and Answers for 2025 
• SQL Server, PostgreSQL, MySQL: What's the Difference?
• PostgreSQL vs. MySQL: Choosing the Right Database for Your Project 
• A Comprehensive NoSQL Tutorial Using MongoDB