1. Cassandra uses columns and tables to store data, while MongoDB stores data in JSON-like documents.
  2. Cassandra does not completely support secondary indexes, while MongoDB mostly relies on indexes for fetching data.
  3. Cassandra has its own query language (CQL), while MongoDB supports popular third-party languages, such as Python and Java.
  4. Cassandra relies on third-party tools for aggregation, while MongoDB has a built-in aggregation framework.
  5. Cassandra uses a distributed architecture, which makes it highly available, while MongoDB relies on a master-slave architecture, which gives it lower fault tolerance.
  6. Cassandra is more suited for people looking for MySQL-style databases with more scalability, while MongoDB is best for storing unstructured data.
  7. Cassandra was created in 2008 and comes from the Apache Software Foundation. In comparison, MongoDB was launched by MongoDB, Inc. in 2009. 

Both Cassandra and MongoDB are NoSQL databases that offer enterprises reliable scalability for modern data needs. The two database systems have similar launch periods. Cassandra came into being in 2008, and MongoDB followed a year later. Overall, Cassandra and MongoDB are both open-source with a sizeable community for support. But that's where the similarity ends between the two. 

This article will highlight Cassandra vs. MongoDB and outline the pros and cons of each database management system. Read on to learn more about the Cassandra vs. MongoDB matchup, discover how the two databases stack up against each other, and how Integrate.io and its new ETL platform can help elevate the user experience for users of either of these two databases. 

Cassandra vs. MongoDB: Everything You Need To Know

Both databases have high-profile customers. Behemoths such as Netflix, Instagram, and Hulu rely on Cassandra for their data storage needs. Similarly, giants such as Google, Adobe, and Paypal use MongoDB. However, the two databases differ in terms of how they store data, replication of data, and other functionalities. Here are some key differences between Cassandra and MongoDB.

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

Table of Contents

Cassandra vs. MongoDB: Data Structure

Cassandra is closer to a relational database in terms of how it stores data. It is a column-oriented database that stores data in tables. However, unlike relational databases, you can create columns and tables on the fly. Moreover, every row in Cassandra does not need to have the same columns. The tabular database relies on the primary key to fetch data.

MongoDB, on the other hand, is an object-oriented database. It uses BSON (Binary JSON) to store data. MongoDB can support varied object structures, and you can even create nested object structures. Since you don't need a schema for JSON, MongoDB is much more flexible compared to Cassandra. However, you can create a schema in MongoDB if needed.

Cassandra vs. MongoDB: Secondary Indexes

Secondary indexes are useful for accessing data that is a non-key attribute. Cassandra does not fully support secondary indexes. It relies on primary keys to fetch information.

MongoDB prefers indexes for querying. It fully supports secondary indexes that can enhance query speeds. It is possible to query any property of an object, including nested objects, really quickly.

Cassandra vs. MongoDB: Query Language

Cassandra employs Cassandra Query Language (CQL) for fetching data. CQL is very similar to SQL. Database administrators who are familiar with SQL should find it very easy to pick up CQL.

MongoDB gives a lot more options in query languages since it stores data in JSON-like documents. Administrators can query MongoDB using the Mongo shell, PHP, Perl, Python, Node.js, Java, Compass, and Ruby.

Cassandra vs. MongoDB: Scalability

Cassandra allows for multiple master nodes, which greatly enhances its write scalability. You can specify the number of nodes you want in a cluster. The more the number of nodes, the more scalable your database is.

MongoDB only allows a single master node. All the other nodes in a cluster are slaves. While data is being written to the master node, you can only perform read operations on the slave nodes. Due to its master-slave architecture, MongoDB is not as scalable as Cassandra. However, you can improve the scalability of MongoDB through sharding techniques. Those might require some setting up, though.

The difference in how the two interpret master nodes also decides their fault tolerance. Since Cassandra allows multiple masters, you can write to a cluster even when a node fails. With MongoDB, you might have to wait 10 to 40 seconds for write operations in case of a node failure since it only allows a single master. In effect, Cassandra beats MongoDB when it comes to availability.

Cassandra vs. MongoDB: Aggregation

Aggregation allows you to run complex queries. Cassandra does not have an aggregation framework. Administrators need to use third-party tools such as Hadoop and Spark for aggregation.

MongoDB, in comparison, has a built-in aggregation framework. It can run an ETL pipeline to aggregate stored data and return results. However, the database's built-in aggregation is only efficient for medium traffic. As you scale, handling the aggregation framework becomes more complex.

Cassandra vs. MongoDB: Performance

A lot of factors go into how a database performs. For example, the kind of schema you use plays a pivotal role in query speeds. Similarly, input and output load characteristics influence the performance of a database. To get a better gauge of the performance of both the Cassandra and MongoDB platforms, let's take a look at what actual users of the two platforms have to say. 

Cassandra Performance 

As of May 2022, Cassandra has a ranking of 4.0/5.0 stars on G2 and has garnered 33 reviews by users.

One user, an administrator of computer software at a small business, left this review on G2 about Cassandra, "Its NoSQL structure provides us with a quick way to store millions of rows of statistics of our users. The query language is easy to understand. The setup is a little roundabout. Cost to run service on your own can get pricy."

An additional user left the review, "If you are looking for a no SQL database with high scalability and fast data writes, with almost 0 downtimes, Cassandra is the solution." 

The reviewer went on to list their favorite features of Cassandra, a list that included the following features:

  • Scalable database
  • Open-source
  • Wide community support
  • Blazing-fast speeds
  • The goodness of SQL with enhanced CQL
  • Can store huge amount of data

MongoDB Performance 

As of May 2022, MongoDB has a ranking of 4.5/5.0 stars on G2, and 444 users have left reviews about the database. 

A software engineer and user of MongoDB left this review on G2, "MongoDB cloud is a very easy to use NoSQL environment where you can set up your no SQL database instances easily and quickly without installing software in your local machine. MongoDB cloud has all the tools you want in no SQL environment, but the monthly cost is somewhat high for a small project."

An additional user had this to say about the MongoDB database in their review, "I have been using MongoDB for the last three years, and I can say without a doubt that it's the best DB out there in the NoSql marketplace. Simply because of its flexible document schemas, easy deployments/managements, and more data agnostic environment."

Overall, when it comes to performance, both MongoDB and Cassandra are well-favored by their user base. However, neither database offers everything that its users desire. That's where Integrate.io comes into the picture. Integrate.io and its new ETL platform with reverse ETL capability have a lot to offer organizations in the process of data management and data integration, helping to process and utilize data more efficiently. 

Cassandra vs. MongoDB: Licensing

Both databases are available as free, open-source software. Third-party vendors, such as Datastax, offer enterprise-grade Cassandra. MongoDB, on the other hand, is overseen by its namesake software company. Both of them are available on subscription models in different tiers, starting from basic to more advanced. You can also find Cassandra and MongoDB in the AWS marketplace and host them on public clouds.

Who Wins the Battle Between Cassandra vs. MongoDB?

Both databases have their pros and cons. The database that you should choose depends on your priorities. In terms of availability, Cassandra has the upper hand. Its highly distributed architecture means you can continue writing to a cluster even when nodes fail. MongoDB, on the other hand, is great for storing unstructured data. The schema-free architecture makes it well-suited for high-speed caching and logging. Real-time analytics and streaming applications rely on high-speed caching and logging operations. MongoDB is also great for fast-query times since it supports secondary indexes. If you are expecting your data operations to scale rapidly, though, Cassandra will be a better fit.

Let Integrate.io Help With Your Database Needs

Whichever database you pick for your use case, Integrate.io can quickly integrate it with your other data sources for fast data analysis. Our new ETL platform is a top-level ETL platform with extract, transform, and load capabilities along with reverse ETL and ELT capabilities. The Integrate.io platform also features a drag-and-drop interface that lets you build ETL pipelines within minutes without any complex coding involved. 

Overall, Integrate.io makes data analysis, data processing, building data pipelines, and moving large amounts of data or datasets from data warehouses seem like a breeze. So, whether your organization needs guidance with data integration, relational databases, APIs, SQL, Azure, AWS, Apache, unstructured data, or real-time data analytics, Integrate.io is here to help you tackle all of your data management needs.

Are you ready to discover more about the many benefits the Integrate.io platform can add to your organization's database management system? If so, contact our team to schedule an intro call today. We look forward to helping you get more out of your valuable data types and helping you reach all your data management goals.