One of the most important parts of the function of any company is a secure database. With phishing attacks, malware, and other threats on the rise, it is essential that you make the right choice to keep your data safe and process it effectively. However, it can be extremely difficult to choose from the wide variety of database solutions on the market today. Two commonly used options are MongoDB and PostgreSQL.

In the realm of modern data management systems, MongoDB and PostgreSQL have emerged as titans, each offering unique strengths and capabilities to meet the diverse needs of businesses and developers worldwide. As organizations navigate the ever-expanding landscape of databases, choosing the right one becomes a critical decision that can shape the efficiency, scalability, and success of their applications.

Here are the key points from our Mongo DB vs. PostgreSQL comparison:

  •            MongoDB is an open-source non-relational database system that falls under the NoSQL category.
  •            PostgreSQL is a relational database management system.
  •            Each database management solution has its own ideal use cases.
  •            All databases function better with a business-wide focus on data integrity.

What do you need to know about MongoDB vs. PostgreSQL? In this article, we delve into the intricate nuances of MongoDB and PostgreSQL, putting them against each other in a battle for superiority. Join us as we explore the key features, functionality, and performance of these two industry-leading database management systems, ultimately unraveling the MongoDB vs. PostgreSQL dilemma and providing you with the insights necessary to make an informed choice for your next project.

Table of Contents


What Is MongoDB?

MongoDB is a schema-free document high-performance database offering both free and paid plans. As a document database, MongoDB has a different structure and syntax than the traditional RDMS (Relational Database Management System). It is a NoSQL (Not Only SQL) database. You can use it on various operating systems.

NoSQL databases are generally simpler by nature, so MongoDB is relatively easy to learn for those with any prior programming experience. Document databases process data as JSON-type documents. This data is semi-structured rather than fully structured.

It can process large volumes of data faster than many other solutions. It also means that users can search and process data in all different stages of processing and in various formats and structures, thereby giving the database a higher level of flexibility than databases that handle traditional relational data. Users can access the data and make changes or updates to the schema as needed, unlike with the SQL database model where users can only access and store data once it has been processed and properly formatted.

MongoDB uses BSON (Binary JSON). BSON allows for certain data types that are not used with regular JSON, such as long, floating-point, and date. MongoDB also offers an alternative query language to SQL called MQL. MQL brings many of the same functions as SQL along with additional support for a variety of programming languages.

The database features a distributed architecture, meaning that components function across multiple platforms in collaboration with one another. This also means that MongoDB has nearly unlimited scalability since it can be scaled across more than one platform as needed. That is one of the many factors that differentiate MongoDB from a relational database: relational databases only scale vertically and are expensive because at some point they require multiple servers in order to scale multiple replicas of the database. MongoDB can scale horizontally.

MongoDB was programmed in C, C++, and JS.

MongoDB offers client-side, field-level encryption through TLS and SSL (Transport Layer Security and Secure Sockets Layer). TLS and SSL are both internet encryption protocols that make HTTP (Hypertext Transfer Protocol) turn into HTTPS (Hypertext Transfer Protocol Secure). In fact, TLS is simply an upgraded SSL of sorts, created to reduce security vulnerabilities. Users can thereby encrypt all MongoDB network traffic. Additionally, MongoDB has various safeguards to ensure the proper authentication of user identities.

MongoDB offers community support, tutorials, and, for a price, full training and upgrading under the supervision of a support engineer.

MongoDB has very fast task fulfillment, in particular, thanks to the fact that the data is only semi-structured. According to various reviews, it is one of the faster solutions on the market, even when dealing with huge volumes of data on a regular basis. This makes it ideal for situations where data needs to be real-time or near real-time, thereby enabling companies to get a holistic view of their business in real-time and improve data optimization.

MongoDB can be hosted on such cloud platforms as Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure.

Common use cases for MongoDB include customer analytics, content management, business transactions, and product data. Thanks to its ability to scale, the database is also ideal for mobile solutions that need to be scaled to millions of users.

The latest version of MongoDB has new features, such as support for automatic data archival, delete operations, and time series dataset distribution across shards. Launched in 2007, MongoDB now serves some of the world’s biggest companies, including EA, eBay, and Shutterfly.

For more information on Integrate.io's native MongoDB connector, visit our Integrations page.

What Is PostgreSQL?

PostgreSQL is a 100% free and open-source ORD (object-relational database) that dates back to 1987, making it significantly older than MongoDB. Instead of storing data like documents, the database stores it as structured objects. It follows a traditional syntax and schema for SQL databases.

A schema is a template or structure you can apply to databases using a set vocabulary. The schema contains various schema objects, including tables, columns, keys, etc. You must structure data before loading it into such a database. While this tends to require more time, it can also put the data into a more manageable and readable format.

PostgreSQL has a monolithic architecture, meaning that the components are completely united. This also means that the database can only scale as much as the machine running it. It was programmed in C, one of the most popular programming languages. The code is open-source and accessible to developers. PostgreSQL offers community support and only offers additional paid support options through certain other companies.

Postgres includes basic file protection, the ability to restrict client connections by IP address, and compatibility with various other external packages intended to make it even more secure.

Use cases for PostgreSQL include bank systems, risk assessment, multi-app data repository, BI (business intelligence), manufacturing, and powering various business applications. It is ideal for transactional workflows. Also, PostgreSQL has fail-safes and redundancies that make its storage particularly reliable. This means that it is perfect for important industries like healthcare and manufacturing.

Both databases use different syntax and terminology to perform many of the same tasks. Where PostgreSQL uses tables, MongoDB uses collections. Where PostgreSQL uses rows to record data, MongoDB uses documents, etc. They also have many features that distinguish them from one another.

The most recent version of PostgreSQL has new features such as improved performance for queries and performance gains and space savings when B-tree index entries become duplicated. Companies like Groupon, Trivago, and Revolt use PostgreSQL to manage data.

Our no-code data pipeline platform comes with out-of-the-box connectors for both MongoDB vs. PostgreSQL, helping you unify your data and gain more meaningful insights from your data warehouse. Try it for yourself with a 14-day trial now.

Key Features in MongoDB vs. PostgreSQL

Here are some things to consider when comparing MongoDB vs. PostgreSQL:

Acid Compliance

MongoDB has the potential for ACID compliance, while Postgres has ACID compliance built-in. ACID (atomicity, consistency, isolation, durability) are principles or components that work toward data validity, especially in databases intended for transactional workflows.

Data Structure

MongoDB uses collections for the same purpose that Postgres uses tables. These collections include options for setting validation rules and maximum sizes. Postgres describes tables in a very specific language and structures the data in such a way that the database or an ETL tool can process it.

Another example of the difference in terminology and syntax between the two is that MongoDB uses documents to obtain data while Postgres uses rows for the same purpose.

While MongoDB does not support FOREIGN KEY constraints, PostgreSQL does. A foreign key can be one column or a group of columns that you can use to create a link in data from multiple tables at the same time. MongoDB aggregation pipelines are made up of multiple stages to transform data. Postgres uses GROUP_BY to run queries while MongoDB uses the aggregation pipeline.

Data Set Management

MongoDB uses redundant replica sets, and Postgres performs synchronous or 2-safe replication to maintain data sets. SQL databases like Postgres use joins to combine data from multiple tables into a single table. There are four different types of joins that you can use: full, left, right, and inner. Assuming that you have two tables from which you want to join some data but not all, you would use left, right, or inner to combine both tables either into your first table, your second table, or a portion of both tables into a third table. if you want to join all of the data simultaneously into one table, you would use a full join.

MongoDB uses indexes, which are only one component of a join. The database is not designed to perform regular joins. Indexes are a type of data structure that can store a minimal amount of data in an easily readable form. They help you to resolve queries with greater efficiency by making the data simpler and thereby easier to scan.

Here is a side-by-side comparison of MongoDB vs. PostgreSQL:

MongoDB

PostgreSQL

Schema-free

SQL-based but supports various NoSQL features

Document database

Relational database

Uses BSON

Uses SQL

Distributed architecture

Monolithic architecture

Potential for ACID compliance

ACID-compliant

Uses collections

Uses tables

Uses documents to obtain data

Uses rows to obtain data

Does not support foreign key constraints

Supports foreign key constraints

Uses the aggregation pipeline for running queries

Uses GROUP_BY

Redundant replica sets

2-safe replication

Uses indexes

Uses joins

Since these are two of the most important and common database solutions on the market today, it is essential that you know exactly what you need for your company and how to use your database to its full potential.

When to Use MongoDB vs. MySQL

Common Use Cases

Determining the most suitable database management system for a specific use case is crucial in maximizing the efficiency and functionality of an application. MongoDB and MySQL, two prominent contenders in the database landscape, cater to different use cases based on their unique strengths and capabilities.

MongoDB shines in scenarios where flexibility, agility, and scalability are paramount. Its document-oriented data model allows for seamless handling of unstructured and semi-structured data, making it an ideal choice for applications dealing with large volumes of constantly evolving data, such as content management systems, real-time analytics, and social media platforms. MongoDB's ability to store complex hierarchical structures and support nested data enables developers to work with dynamic and diverse datasets, providing unmatched flexibility and agility.

On the other hand, MySQL excels in use cases that require robust transactional support and strong data integrity. It is a preferred choice for applications that heavily rely on complex queries, strict ACID compliance, and relational data models, such as e-commerce platforms, banking systems, and inventory management systems. With its mature and proven architecture, MySQL ensures data consistency, reliability, and accuracy, making it a trusted option for applications that demand secure and structured data storage.

Understanding the specific requirements and nature of your project is vital in determining whether MongoDB or MySQL is the right fit. Evaluating factors such as data structure, query complexity, scalability needs, and expected growth will help you make an informed decision, aligning the database management system with the unique demands of your use case.

User-Friendliness

User-friendliness plays a pivotal role in the adoption and productivity of a database management system, as developers and administrators seek intuitive interfaces and streamlined workflows that simplify their tasks. When comparing MongoDB and MySQL in terms of user-friendliness, different aspects come into play.

MongoDB offers an inherently developer-friendly experience. Its flexible document model, based on BSON (Binary JSON), aligns well with modern programming paradigms and eliminates the need for complex object-relational mapping (ORM) layers. MongoDB's query language, MongoDB Query Language (MQL), resembles JSON syntax, making it intuitive for developers to retrieve and manipulate data. Additionally, MongoDB's extensive documentation, vibrant community, and wealth of online resources contribute to a supportive ecosystem that facilitates learning and problem-solving.

MySQL, with its long-standing presence in the market, boasts a mature and robust user interface. It offers multiple options for managing and interacting with databases, including the popular MySQL Workbench and command-line tools like MySQL Shell. These tools provide a graphical environment for database administration, query execution, and performance tuning, catering to both novice and experienced users. MySQL's familiar SQL language, widely adopted across the industry, ensures ease of use and compatibility with existing skills and knowledge.

Ultimately, the choice between MongoDB and MySQL in terms of user-friendliness depends on the familiarity and preferences of the development team, as well as the level of support and resources available. Understanding the learning curve, available tools, and community support associated with each system can contribute to a smoother adoption process and enhanced productivity in the long run.

Scalability

Scalability is a critical factor when evaluating database management systems, as it determines the system's ability to handle increasing data volumes, user loads, and transaction rates. Both MongoDB and MySQL offer scalability options, but they differ in their approaches and capabilities.

MongoDB embraces horizontal scalability as a core principle. With its distributed architecture and native support for sharding, MongoDB allows for seamless distribution of data across multiple servers, enabling linear scalability. This makes MongoDB an excellent choice for applications experiencing rapid growth, as it can effortlessly handle high traffic and massive datasets by adding more servers to the cluster. Additionally, MongoDB's auto-sharding feature simplifies the

Perform ETL to PostgreSQL and MongoDB with Integrate.io

 

The main differences between MongoDB vs. PostgreSQL have to do with their systems, architecture, and syntax.

  • MongoDB is a document database, while Postgres is a relational database management system.
  • MongoDB has a distributed architecture, while PostgreSQL has a monolithic architecture.
  • Postgres uses SQL, while MongoDB uses BSON.

One of the biggest issues that companies have while processing data from either database is the time and complexity involved. ETLing (extract, transfer, and load) big data into MongoDB vs. PostgreSQL databases often involves extensive coding and complicated, time-consuming processes. Plus, you need to comply with data governance frameworks when moving data from one location to another, or you could face hefty penalties. Other data integration methods like ELT and ReverseETL can be just as challenging if you lack a large data engineering team.

That is why Integrate.io offers a data integration solution that lets you transform and manage your data in both MongoDB and Postgres. Using a drag-and-drop-based interface, Integrate.io enables users with zero coding experience to build data pipelines and effectively clean and transfer high-volume data sets. This entire process doesn’t require complicated code, so you can move data to the database of your choice without any data engineering experience. Integrate.io does all the hard work for you. Choose from data integration methods such as ETL, ELT, ReverseETL, CDC, and more.

There are other benefits of using Integrate.io when choosing between MongoDB vs. PostgreSQL. The platform has a unique pricing model that charges you for the number of connectors you use and not the data you consume. Plus, you can access world-class support and benefit from over 100 out-of-the-box connectors that move data between relational databases, transactional databases, customer relationship management (CRM) systems, and more.

With Integrate.io, your company can optimize data integration tasks and use your existing database solution to its full potential without having to master complex coding languages like Python and Java. While you decide between MongoDB vs. PostgreSQL, contact Integrate.io today for a comprehensive, 14-day trial of our services to try out our platform for yourself, and remember to schedule an intro call with our expert team.