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 in order to keep your data safe and process it effectively. However, it can be extremely difficult to choose among the wide variety of database solutions on the market today. Two commonly-used options are Mongodb and Postgresql.

What do you need to know about MongoDB vs. PostgreSQL? This article will take you through a comparison of the key features, functionality, and performance of each.

Table of Contents

  1. What is MongoDB?
  2. What is PostgreSQL?
  3. Key Features in MongoDB vs. PostgreSQL
  4. Perform ETL to PostgreSQL and MongoDB with Xplenty

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What is MongoDB?

MongoDB is a schema-free document 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. 

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. This means that 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 traditional relational databases. 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 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 bringing 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 because relational databases only scale vertically and are expensive since 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 both community support 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 the moment.

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. The database is also ideal for mobile solutions that need to be scaled to millions of users, thanks to its ability to scale. Another major use case is platforms offering data-as-a-service. MongoDB can update data in real-time, permitting users to view new information as it comes in. Finally, the IDP (Intelligent Data Platform) offered by MongoDB combines the database with other, complementary technologies to become a total IoT (Internet of Things) platform to support IoT applications.

What is PostgreSQL?

PostgreSQL is a 100% free and open-source ORD (object-relational database). Instead of storing data like documents, the database stores it as structured objects. It follows a traditional syntax and schema for SQL databases. Schema is effectively a template or structure that you can apply to databases using a set vocabulary. The schema contains various schema objects, including any 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. 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. 

Key Features in MongoDB vs. PostgreSQL

  • 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 towards data validity, especially in databases intended for transactional workflows. 
  • MongoDB uses collections for the same purpose that Postgres uses tables. These collections include options for setting validation rules and setting 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. Since these constraints disallow any actions that ruin 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. 
  • 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. 
  • MongoDB uses redundant replica sets, and Postgres performs synchronous or 2-safe replication to maintain data sets. You can use replica sets to record and ‘replay’ processes on an as-needed basis. Synchronous replication involves multiple repositories or systems updating at the same time. Since the PostgreSQL database updates both records at the same time, in the same way, you can reduce errors and the user then has a complete and accurate backup. 
  • 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, or 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 very small amount of data in an easily readable form. They help you to resolve queries with greater efficiency through making the data simpler and thereby easier to scan. 

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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. That is one of the ways Xplenty can help you. 

Perform ETL to PostgreSQL and MongoDB with Xplenty

In summary, the main differences between MongoDB and 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; and Postgres uses SQL, while MongoDB uses BSON. 

For those who already have a rudimentary understanding of JavaScript, MongoDB has a shorter learning curve, whereas those who have long experience in SQL databases may find it easier to adjust to Postgres. Both are growing in popularity as comprehensive database solutions for a variety of industries. However, one of the biggest issues that companies have while processing data from either database is the time and complexity involved. 

ETL (extracting, transferring, and loading) data into MongoDB or PostgreSQL databases often involves extensive coding and complicated, time-consuming processes. Additionally, since MongoDB has unusual syntax and NoSQL support, many ETL providers may not have optimized their solutions to deal with its specific challenges. 

That is why Xplenty offers a low-code ETL solution to allow you to transform and manage your data in both MongoDB and Postgres. Using a drag-and-drop-based interface, Xplenty permits users with zero coding experience to build data pipelines and effectively clean and transfer high-volume data sets. 

Using Xplenty, your company can streamline ETL tasks and use your current database solution to its full potential. Contact Xplenty today for a comprehensive, 7-day demo of our services.