Cassandra vs. MongoDB: Knowing the Differences

Welcome to the complete review of two considerable monsters in Databases: Apache Cassandra and MongoDB. Developers, be prepared; I have designed the article so that you can make a reasoned decision for your journey ahead by the end. 

Currently, with the trend of NoSQL databases, most organizations have started to move ahead with it. Among various other databases, Apache Cassandra and MongoDB stand out as top choices for many. 

Let’s start with an introduction to Cassandra and then move ahead with MongoDB. And Finally, by comparing the two well-known NoSQL databases side by side in terms of the data model, indexing, Query Language, Transactions, Concurrency, Security, High Availability, and Scalability, I will aim to provide developers with a clear guide and a better understanding of their differences.

Without any delay, let’s delve into the kingdom of NoSQL databases and explore the strengths and capabilities of the two warriors: Apache Cassandra and MongoDB. Get Ready for this exciting tussle! 

Overview of Apache Cassandra

Thousands of organizations aim for availability and scalability without compromising performance when settling down for a perfect platform for critical data flow and storage. The answer to this is Apache Cassandra! 

Apache Cassandra is an open-source NoSQL database. With its capabilities like linear scalability and security, I believe Cassandra has created a good space in the information technology market. 

Another factor to be considered is that Apache Cassandra supports low latency, which means it can stand up to an entire data center outage, and that too with no data loss. That’s not the end; Cassandra can track the DML, DDL, and DCL activity with minimal effect on the workload performance. 

To gain a deeper understanding of Cassandra, it is also essential to explore its History. Cassandra was developed by Avinash Lakshman to power the Facebook Inbox Search Feature. As the need for a strong and distributed database grew, Facebook released Cassandra as an Open-Source Project, which later became an Apache Incubator Project.

Coming towards its Data Model, Cassandra combines features like key-value and tabular databases. It stores data in rows, and each row belongs to a table. The Partition Key and the remaining columns are the two parts of Cassendra’s primary key. This unique design allows for efficient data organization within each partition, and specific columns can be separately indexed, allowing faster data access.

For better organization, applications can control column sorting. This specific approach permits efficient and scalable data retrieval in Cassandra’s distributed environment. To be accurate, in  Cassandra, a table is like a distributed map obtained by a key.

I am presenting the key features of Cassandra graphically for better comprehension. Please refer to the image below:

As I conclude this section on Apache Cassandra, it’s important to highlight one noteworthy aspect: CQL (Cassandra Query Language)! CQL has revolutionized user interaction with the database.

Its user-friendly interface makes it a simplified version of SQL. With CQL, Cassandra is much more intuitive and accessible to all. You can explore Apache Cassandra monitoring tools to sharply observe your database performance.

Let’s drift toward MongoDB and explore its fabulous strengths and capabilities.

Overview of Mongo Database

In this section, I will introduce MongoDB and its essential information and features. MongoDB has earned a reputation as one of the most reliable databases, making it a popular and versatile choice for managing NoSQL data.

Initially, a US Software Company, “10gen,” started to develop MongoDB in 2007 as a PaaS product. Later the Mongo project shifted to an open-source development model and was officially released on 11 February 2009. After a while, “10gen” changed its name to MongoDB Inc, reflecting the database’s central focus.

MongoDB stands out with its flexible document-based data model, storing data in a BSON (Binary JSON) format. This unique approach allows MongoDB to efficiently handle semi-structured and unstructured data, making it exceptionally well-suited for applications with rapidly changing requirements. MongoDB proves to be a perfect fit for dynamic projects.

You will be able to run MongoDB with MongoDB Atlas managed cloud services. Alternatively, you can choose the MongoDB Community edition, which is open-source and free.

For enterprise-level requirements, MongoDB offers the MongoDB Enterprise Advanced Subscription. This subscription provides additional features, support, and advanced security options, making it ideal for businesses with mission-critical applications.

So far, I have covered a lot about MongoDB, but there’s an essential aspect I want to highlight: MongoDB supports concurrency through optimistic and document-level locking. But that’s not all! MongoDB gives you the ability to perform complex queries on the data. 

The fact that truly makes MongoDB a powerful and versatile database is its query language, which can support various operators, aggregations, and projections. These features and query capabilities make MongoDB a top choice for developers.

Next, I will move ahead with the top features of MongoDB that will essentially broadcast the conclusion to you. Please follow the below highlights of the top features explained in an understandable perspective.

✅ Ad-hoc queries for real-time analytics with variable-dependent results.

✅ Appropriate indexing for optimized query execution and performance.

✅ Replication for enhanced data availability, stability, and disaster recovery.

✅ Sharding for distributing large datasets across multiple shards, ensuring scalability.

✅ Load balancing to handle concurrent read and write requests for better performance and consistency.

Overall, MongoDB is rightfully the correct contender for Apache Cassandra. Over time, MongoDB has proved itself reliable in every sense of the word. Developers personally love MongoDB due to its performance and security. Now let’s proceed toward the warriors’ tussle and determine which wins.

Differences Between MongoDB and Cassandra

Apache Cassandra and MongoDB are the most popular No-SQL databases with distinct strengths and characteristics. Cassandra follows a wide-column store approach, while on the other hand, MongoDB is document-oriented. 

MongoDB provides various options for indexing, like single-field, geospatial, compound, and text indexes, whereas Cassandra supports secondary indexes with some limitations. I would also like to highlight that the query language differs for both databases. Here, Cassandra uses CQL; on the other side, MongoDB engages MQL (MongoDB Query Language).

Cassandra’s distributed design and linear scalability are critical advantages for high scalability and availability, while MongoDB supports replica sets for high availability. And when it comes to cloud offerings, MongoDB stands out with MongoDB Atlas, while Cassandra lacks a dedicated managed service. 

The choice between Cassandra and MongoDB depends on the specific requirements of the application and its data management needs. Cassandra excels in distributed architecture and high availability, making it suitable for large-scale applications. On the other hand, MongoDB’s flexible data model, powerful query language, indexing options, and support for ACID transactions make it a popular choice for various use cases. 

I thought to insert this section for your better understanding and clarification. The representation of the differences is in tabular form, which might be a helpful guide. 

The above table concisely compares the main differences between Cassandra and MongoDB. The choice between the two databases depends on specific use case requirements and preferences.

Final Words

Different organizations have trusted both databases according to their requirements. Organizations like Apple, Netflix, Airbnb, Uber, etc, use Apache Cassandra as their database.

On the other hand, companies like Adobe, IBM, Bosch, LinkedIn, etc, have trusted MongoDB for their journey. The future scope for both databases is bright as Cassandra will undoubtedly continue to grow in Iot, while MongoDB’s future may involve integrating machine learning capabilities to enable advanced data analysis and predictive modeling within the database.

Both Cassandra and MongoDB are expected to continue evolving and expanding their capabilities to meet the growing demands of modern data-driven applications and provide even better solutions for businesses and developers worldwide. The choice depends on specific needs and scalability requirements. Therefore, choose wisely!

Next, check out a detailed article on MongoDB vs. MariaDB vs. MySQL.