MongoDB vs PostgreSQL: 8 Critical Differences


In the modern world today, competition between companies is very common, especially when they are offering similar products. In the competitive field of Data Analytics, offering efficient products and services and having a majority customer share in the market does help determine the profit of the company. When it comes to the field of Database Management, the choice of MongoDB vs PostgreSQL is a relatively tough one.

MongoDB is a popular NoSQL database developed by MongoDB Inc. It is a source-available cross-platform document-oriented database program that uses JSON (JavaScript Object Notation)-like documents and optional schemas to store your data. PostgreSQL, on the other hand, is a free, open-source RDBMS (Relational Database Management System) that was developed at the University of California, Berkley. Both these technologies are leveraged by organizations of all scales, both big & small, and depending on the situation, one can dominate over the other. 

This article provides you with a comprehensive analysis of both databases and highlights the major differences between them to help you make the MongoDB vs PostgreSQL decision easy. It also provides you a brief overview of both databases along with their features. Finally, it highlights a few challenges you might face when you use these databases. Read along how you can choose the right database for your organization.

Table of Contents

Introduction to MongoDB

MongoDB is a schema-free NoSQL database that uses JSON-like documents with optional schemas to store your data. It is quite different from the traditional RDMS in terms of syntax and structure. It was developed by MongoDB Inc. As NoSQL databases are relatively easy to use, MongoDB is also simple to use for individuals that have no prior programming experience. Data is processed in a semi-structured manner so that you can process large volumes of data simultaneously. It was designed using C, C++, and JavaScript and is hosted on Cloud platforms like Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure.

MongoDB uses BSON (Binary JSON) and MQL, an alternative language to SQL. BSON allows for certain data types that are not used with regular JSON, such as long, floating-point, and date. MQL too offers similar features as SQL with some additional capabilities. This is done because documents are processed as JSON-type documents.

Key Features of MongoDB

MongoDB houses a wide range of services that make it a better solution when compared to other databases. Some of these features are:

  • In MongoDB, you can search by field, range query, and regular expressions. This way it adds support for ad hoc queries.
  • It is a schema-less database written in C++ that provides high performance.
  • It can index any field in a document and supports Master-Slave replication.
  • It has an automatic load configuration feature to group similar data in its database.
  • It stores files of any size easily without complicating the stack and is easy to administer in case of any failure.
  • It uses JavaScript instead of Procedures.
  • MongoDB also supports the JSON data model, auto-sharding, and built-in replication for high scalability and availability.

To learn more about MongoDB, click this link.

Introduction to PostgreSQL

PostgreSQL, also known as Postgres is a free, open-source RDBMS that emphasizes extensibility and SQL Compliance. It was developed at the University of California, Berkeley, and was first released on 8th July 1996. It was the successor to the Ignes database. Instead of storing data like documents, PostgreSQL stores it as Structured objects. It follows the traditional SQL format and syntax.

It is programmed in C and follows a monolithic architecture, which means that the components are completely united and work systematically. It offers community support along with additional support to some of its paid customers. It is widely used in the healthcare, banking, and manufacturing industries due to its innovative backup mechanisms.

Key Features of PostgreSQL

PostgreSQL houses some unique features that make it a good alternative compared to other traditional RDBMSs. Some of these features are:

  • PostgreSQL supports a wide variety of data types, document types, and customizations.
  • It has a monolithic architecture where all the components work together in an automated manner.
  • It is ideal for transactional workflows such as in bank systems, for performing risk assessments, BI (Business Intelligence), and powering various business applications. 
  • It has multiple fail-safes and redundancies that make storage reliable.
  • It is open-source and so any user can use all of its features, free of cost.
  • It has limited scalability as its processing power depends on the machine it runs on.
  • It has a robust access control system that has additional features like row and column-level security and multi-factor authentication with certificates.
  • It runs successfully on major operating systems and is ACID (Atomicity, Consistency, Isolation, and Durability) compliant.
Simplify Data Analysis with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports MongoDB and PostgreSQL, along with 150+ data sources (including 40+ free data sources), and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Get Started with Hevo for free

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!

Factors that Drive the MongoDB vs PostgreSQL Decision

Now that you have a basic idea of both technologies, let us attempt to answer the MongoDB vs PostgreSQL question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the MongoDB vs PostgreSQL decision:

1) MongoDB vs PostgreSQL: ACID Compliance

MongoDB has the potential for being ACID Compliant whereas PostgreSQL has it built-in. The ACID properties are the fundamental properties of databases so that transactions can be tracked properly.

MongoDB is a document database and uses BSON for processing its data whereas PostgreSQL is a relational database that uses traditional SQL for its processing.

2) MongoDB vs PostgreSQL: Architecture

MongoDB is a schema-free NoSQL database that supports a distributed architecture. MongoDB uses collections to enforce different rules and triggers to maintain the relationship between different attributes in the database. The architecture of MongoDB is shown below.

PostgreSQL follows an SQL-based architecture but supports some NoSQL features as well. Unlike MongoDB, it has a monolithic architecture. To set various rules and triggers on the data, it uses tables. It also structures the data in such a way that the database or an ETL(Extract, Transform, and Load) tool efficiently process the data. The architecture of PostgreSQL is shown below.

3) MongoDB vs PostgreSQL: Syntax

The syntax supported by both databases is quite different from each other. MongoDB, being a NoSQL database, leverages documents to store data, allowing users to access it using MQL. PostgreSQL on the other hand uses an RDBMS structure and SQL to store and access data respectively.

The MongoDB syntax for creating a database, see if it is the current database, and display the database is shown below.

Creating a database:

>use mydb
switched to db mydb

Checking if the database created is the current database:


Displaying the contents of the database:

>show dbs
local     0.78125GB
test      0.23012GB

The MongoDB syntax to insert a record into the database is shown below.

>{"name":"tutorials point"})
>show dbs
local      0.78125GB
mydb       0.23012GB
test       0.23012GB

The PostgreSQL syntax to create the “accounts” table is shown below.

CREATE TABLE accounts (
	user_id serial PRIMARY KEY,
	username VARCHAR ( 50 ) UNIQUE NOT NULL,
	password VARCHAR ( 50 ) NOT NULL,
        last_login TIMESTAMP 

The PostgreSQL syntax to insert a record into the table is shown below.

INSERT INTO accounts (user_id serial, username, password, email, created_on, last_login) 

4) MongoDB vs PostgreSQL: Foreign Key Support

A Foreign Key is a column or a group of columns of one table that references another column (generally the primary key) of another table and establishes a relationship between them. MongoDB does not support Foreign Keys whereas PostgreSQL does support them.

Since these constraints disallow any actions that remove links from one table to another and can stop the insertion of invalid data into foreign key columns, this may be a necessary feature for some users. 

5) MongoDB vs PostgreSQL: Query Processing Methods

MongoDB uses aggregation pipelines to process its queries. These pipelines consist of multiple stages that help transform data. PostgreSQL, on the other hand, uses the GROUP_BY to process and run queries.

6) MongoDB vs PostgreSQL: Maintaining Data

To maintain data, MongoDB uses redundant replica sets. These sets allow you to record and replay processes on an as-required basis. MongoDB uses synchronous replication, which involves multiple repositories or systems that update at the same time.

PostgreSQL uses 2-safe replication to maintain its data. This way, PostgreSQL can update both records at the same time, thus reducing the number of errors and maintaining a complete and accurate backup as well.

7) MongoDB vs PostgreSQL: Relationships Among Tables

Relationships between multiple tables of your database add more value to analysis and storage capabilities. MongoDB uses indexes to connect multiple tables together. Indexes are a type of data structure that can store a very small amount of data in an easily readable form. They are only one component of a join and make your data simple to understand and, thereby help you to resolve any queries with ease.

PostgreSQL uses joins to combine data from multiple tables into a single table. As long as you have 2 tables, you can use joins to combine them in PostgreSQL. Similar to traditional SQL, there are 4 types of joins in PostgreSQL- Inner, Left, Right, and Full Join. If you want all the data from both tables into a single table, you can use a Full Join.

8) MongoDB vs PostgreSQL: Pricing Models

MongoDB allows you to choose your pricing model from 3 dedicated price plans. They are:

  • Shared- $0 per month
  • Dedicated- $57 per month
  • Multi-Region- $97 per month

The pricing model of MongoDB Atlas is given below:

MongoDB Atlas Pricing Model
Image Source:

MongoDB also offers an On-Premise pricing model with MongoDB Enterprise Advanced edition.

To learn more about the pricing model of MongoDB, click this link.

As PostgreSQL is free and open-source, anyone can use all its features for free.

Challenges of MongoDB

Now that you have a good idea about MongoDB, it is now important to understand some of the challenges you might encounter while working with MongoDB. The challenges of MongoDB are:

  • It is hard to uncover insights rapidly and simultaneously.
  • MongoDB finds it very hard to integrate data from multiple sources (Big Data) and store that data in a common format.
  • It has a moderate security architecture and is vulnerable to some security challenges.
  • MongoDB finds it difficult to validate data and so faces problems in Data Governance.

Challenges of PostgreSQL

Now that you have a good idea about PostgreSQL, it is now important to understand some of its challenges. The challenges of PostgreSQL are:

  • Although PostgreSQL is easy to deploy on multiple platforms, it does not have the same efficiency on every platform.
  • In case databases need to be upgraded, PostgreSQL doubles their storage capacity.
  • Indexes in PostgreSQL cannot be used to directly return the results of a query.
  • The query execution plans are not cached.
  • In case there are any bulk bounding operations, those processes may become CPU bound.
  • The current data engineering solutions that companies require for data and query processing demand a high learning curve that lacks in PostgreSQL.


This article gave a comprehensive analysis of the 2 popular Database technologies in the market today: MongoDB and PostgreSQL. It talks about both the databases and their features and limitations. It also gave the parameters to judge each of the Databases. Overall, the MongoDB vs PostgreSQL choice solely depends on the goal of the company and the resources it has.

MongoDB can be a good choice if you want your database to be highly scalable and have a high computation & processing power. It can also be used if users lack programming skills as it is very easy to learn and does not follow the traditional SQL syntax. PostgreSQL can be a better choice if you have fewer resources but are well-versed in the traditional SQL syntax and procedures. Other than these few differences, both databases are equally strong in terms of performance and will work well with any organization, customer, or business needs.

In case you want to integrate data from data sources into your desired Database/destination like Generic MongoDB and PostgreSQL and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.

Visit our Website to Explore Hevo

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of learning about MongoDB vs PostgreSQL in the comments section below.

Aakash Raman
Former Business Associate, Hevo Data

Aakash is a research enthusiast who was involved with multiple teaming bootcamps including Web Application Pen Testing, Network and OS Forensics, Threat Intelligence, Cyber Range and Malware Analysis/Reverse Engineering. His passion to the field drives him to create in-depth technical articles related to data industry. He holds a Undergraduate Degree from Vellore Institute of Technology in Computer Science & Engineering with a Specialization in Information Security and is keen to help data practitioners with his expertise in the related topics.

No-code Data Pipeline For your Database