MongoDB vs PostgreSQL: Choosing a Database for Embedded Analytics

Data Engineering
Jul 19, 2023
MongoDB vs PostgreSQL: Choosing a Database for Embedded Analytics

When adding end-user analytics to your SaaS platform, one of the first things on your to-do list is embedding interactive charts and graphs. However, product managers too often skip another essential step on their analytics roadmap.

That is, ensuring a performant data model to power your reports.

What should your data model look like? Which data do your customers want to see? And finally, which database should you store it in? In this article, you’ll learn about two popular database choices: MongoDB and PostgreSQL.

Relational databases vs NoSQL databases

Before we dive into MongoDB and PostgreSQL, it’s crucial to understand the difference between relational and NoSQL databases.

Relational database management systems (RDBMS) organize data into tables with a predefined schema. These tables are linked to each other through key relationships. They use Structured Query Language (SQL) to perform actions like inserting, updating, retrieving or deleting data, and many other data manipulations. Because of this, they are also called SQL databases.

NoSQL databases (“Not Only SQL”) are a newer type of database that works slightly differently than traditional relational databases. These non-relational databases can handle semi-structured or unstructured data - which is useful if you are dealing with rapidly changing big data. Unlike relational data models, they do not require a fixed schema. They can store data in multiple formats:

  • key-value pairs
  • document-oriented structure
  • columnar data structure
  • graph-based data structure

While SQL databases will need more hardware to handle larger amounts of data, a NoSQL database will distribute the load across more servers. Additionally, if you need more servers you can always rely on Ubuntu VPS. For some applications, the scalability that NoSQL databases offer is a huge advantage. And so is the flexibility when your data model evolves, without having to modify your schemas extensively.

Diagram showing the differences between relational databases and NoSQL databases

What is MongoDB

MongoDB is a popular, open-source NoSQL database management system. It is a document-oriented database, which means it stores data in JSON-like documents called BSON (Binary JSON). MongoDB offers high flexibility and scalability, which makes it a popular choice for developers working on applications like content management systems, IoT platforms, and real-time analytics.

MongoDB is available in several forms:

  • MongoDB Atlas: a cloud-based database, which runs on Google Cloud Platform, Microsoft Azure and Amazon AWS.
  • MongoDB Community Edition: a free open-source database that runs locally on Windows, MacOS or Linux
  • MongoDB Enterprise: an advanced version of the community edition, offering more support and features

What is PostgreSQL

PostgreSQL - or Postgres in short - is a popular open-source relational database management system. Just like MySQL and Microsoft SQL Server, Postgres uses the relational model that organizes data into rows (or records) and columns (or attributes). It is one of the most widely used databases in the world, popular with software companies and startups.

You can install PostgreSQL locally on your device, on a server, or run it as a container in Docker. Or, if you prefer to work with your database as a service or API, you can choose managed PostgreSQL hosting through cloud providers like AWS or Google Cloud. That way, you won’t have to manage or maintain any underlying servers.

MongoDB vs PostgreSQL - Architecture

Although MongoDB and PostgreSQL are both open-source databases, they have fundamentally different architectures. Let’s have a deeper look into more specific architectural differences, and what they mean for your use case.

Data model

As mentioned earlier, PostgreSQL is a relational database that organizes its data into tables with fixed schemas. Primary and foreign key constraints make sure the relationships between your tables are defined, avoiding inconsistent data. Although this approach is more rigid, PostgreSQL is the best choice if you want data consistency and integrity.

MongoDB is a document database, which does its data storage in flexible JSON-like documents. Developers can do dynamic and agile data modeling, without enforcing a fixed structure. If your data changes often, and you need more flexibility, MongoDB is a great choice.

Query language

Postgres uses SQL, the standardized language for querying. As it’s been around for decades, any data scientist will know how to write SQL queries. It’s widely adopted, integrates with many other systems, and sets you up for easy migration to other database platforms. And it’s pretty rich in terms of data manipulations and complex operations.

Mongo uses its own MongoDB Query Language (MQL), which is more flexible than SQL in a couple of ways. It can do ad hoc querying, which lets your developers do complex queries without a lot of schema planning or predefined joins. If you have nested and embedded data structures within your documents, you can also query them without complex joins.

For analytics, MongoDB also has a powerful Aggregation Framework to do complex data processing inside the database itself. It can do grouping, filtering, sorting and aggregations like sum, average, count, etcetera.


As your data volume grows, these two databases will handle large datasets in different ways. MongoDB uses sharding, which means it distributes data across multiple machines. These machines could be servers or clusters, called shards. In other words, MongoDB scales horizontally. On the other hand, PostgreSQL uses vertical scaling. All your data is stored on one server, increasing the CPU, storage and memory as you handle more data.

Which one is best for you will depend on your preferred setup. Although MongoDB is considered the most scalable alternative, you may have good reason to keep your data stored in one server. Usually, companies who need strong data integrity and need to comply with certain regulations, like ACID compliance will choose for databases like PostgreSQL. ACID (Atomicity, Consistency, Isolation and Durability) are four properties that ensure a database transaction is completed timely.

Replication and data availability

In terms of data availability, MongoDB offers replica sets. Imagine them as multiple copies of your data, spread across different servers. If one server fails, you’ll still be able to access the data right away. PostgreSQL offers an alternative in the form of streaming replication or logical replication, but it’s not built in. Although this will require more configuration, you will also have more control and flexibility.

Performance and speed

If you ask us which is the most high-performance database, there’s no correct answer. Both can be excellent or horrible, depending on the data model, query patterns, and specific use cases.

In a benchmark study by EnterpriseDB, PostgreSQL outperforms MongoDB in latency and performance, being between 4 and 15 times faster. However, it’s important to evaluate and benchmark against your specific workload to make an informed decision.

Example of performance benchmarking of MongoDB vs PostgreSQL

If you’re working with structured data, complex relationships, and use optimized query execution, PostgreSQL is the way to go. Its indexing options are best for well-defined data structures.

On the other hand, MongoDB is much better with semi-structured and unstructured data. Because it distributes data across servers, MongoDB is also a great pick for applications with massive data growth.

PostgreSQL vs MongoDB - Language and syntax

You or your engineering team may have their preferred sets of programming languages. So you’ll want to know upfront if your database of choice will support your preferred stack.

First off, the good news is that both databases support the most common programming languages in some way or another. MongoDB officially supports the following languages:

  • C
  • C++
  • C#
  • Go
  • Java
  • Kotlin
  • Node.js
  • PHP
  • Python
  • Ruby
  • Rust
  • Scala
  • Swift

If you need support for a different programming language, they also have community-created drivers and libraries.

PostgreSQL supports a variety of popular programming languages, like:

  • .Net
  • C
  • C++
  • Delphi
  • Java
  • JavaScript (Node.js)
  • Perl
  • PHP
  • Python
  • Tcl

It also supports many server-side procedural languages through its available extensions.

We recommend checking the documentation to make sure your preferred language is supported. Both databases also have a vibrant community of contributors, so chances are you’ll find community-generated drivers or libraries to use.

MongoDB vs PostgreSQL - Pricing

Both MongoDB and PostgreSQL are open-source tools, which means you can start using them for free. Of course, you will always have a cost associated for any hosting or servers. The costs for hosting will very much depend on your data volume and requirements, so it’s impossible to make a general comparison.

Rather than selecting a database based on its cost, we recommend choosing the database type that best fits your unique use case and requirements.

Should you use MongoDB or PostgreSQL for embedded analytics?

Looking to add embedded analytics to your SaaS platform, but not sure which database to run them on? The good news is that you can run a great embedded analytics setup on both. The bad news is, however, that speed and performance will depend a lot on your unique situation. The amount of data, the data types, the complexity of relationships between data, and your desired aggregations are only a few factors that impact your decision.

Example of an embedded analytics dashboard

MongoDB is great for high volumes of unstructured data, and can do powerful aggregations and complex analytics operations within the database. However, it you need many complex joins and advanced relational analytics, you may need some additional data processing outside of the database. For complex analytical queries with multiple data relationships, you are better off with a relational database.

PostgreSQL is very strong with complex queries and relational operations, which makes it a great fit for analytics use cases. With its extensive indexing options and query optimization features, you are likely to enjoy good performance. Even with more complex analytical queries. However, you will need to do more schema planning upfront because of its fixed schema. For extremely large datasets, its vertical scaling may not be as efficient as MongoDB’s sharding techniques.

Whichever database you end up choosing, don’t forget to keep in mind the best practices for a data model for embedded analytics. These tips should help you understand your data infrastructure and map out your needs.

If you need help figuring out the right data infrastructure for your use case, our team of analytics experts is here to help. Book a free consultation today!

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