Cassandra vs MongoDB: Real Performance Data, Cost Analysis & When to Use Each

TL;DR

• Cassandra is a wide-column, distributed database optimized for write-heavy workloads and massive IoT/logging/time-series ingestion, capable of handling 1M+ writes/second with high availability.
  
• MongoDB is a document-oriented database optimized for read-heavy workloads and flexible analytics, offering a rich query language, native aggregations, and faster dashboard responsiveness.
  
• When to use Cassandra → Write-intensive, globally distributed, real-time ingestion scenarios.
  
• When to use MongoDB → Complex ad-hoc queries, flexible data models, and real-time dashboards.
  
• When to use both → Cassandra for ingestion, MongoDB for analytics – Knowi connects to both natively without ETL.

Table of Contents

Introduction

Cassandra and MongoDB are both popular NoSQL data sources that were launched around the same time. Cassandra was released in 2008 and MongoDB the following year. Both these data sources are open source, offer large community support and are used by some of the major organizations across the world. However, similarity between them is limited to these factors and they are quite different in the capabilities that they offer. 

This blog highlights the key differences between these data sources to help you choose the data source that is right for your use case. 

What is Apache Cassandra?

Apache Cassandra is a highly scalable and distributed NoSQL database designed to handle large amounts of data across many commodity servers without any single point of failure. It was developed at Facebook and released as an open-source project in July 2008, with Apache later overseeing its development.

Cassandra Architecture & How it Works

Cassandra’s architecture is masterless, all nodes are equal and can handle read/write requests.

Key components:

• Peer-to-Peer Architecture – Eliminates single points of failure.
  
• Data Replication – Configurable replication factor for high availability.
  
• Partitioning – Data is distributed via consistent hashing.
  
• Fault Tolerance – Even if multiple nodes fail, Cassandra continues operations.
  
• Read/Write Path:
  
  • Write Path: Extremely fast due to sequential writes to commit logs and memtables.
    
  • Read Path: Can be slower compared to MongoDB due to eventual consistency and data spread.
    

Cassandra stores data in tables with rows and columns, but unlike traditional RDBMS, each row can have a different set of columns. It provides linear scalability, allowing seamless addition of nodes to handle increasing loads. It can manage petabytes of data and perform multiple concurrent operations in seconds.

Cassandra uses the Cassandra Query Language (CQL), which is similar to SQL, making it easier for developers with SQL experience to transition. It is optimized for write-heavy workloads, making it ideal for scenarios requiring rapid data ingestion and capable of handling large volumes of data efficiently with low latency.

Features:

• Type: Wide-column store
  
• Query Language: CQL (Cassandra Query Language) – SQL-like syntax
  
• Optimized for: Write-heavy workloads
  
• Consistency Model: Eventual consistency (configurable for stronger consistency)
  
• Deployment: On-premises, cloud, or hybrid

Advantages:

1. Open-source with a peer-to-peer architecture eliminating single points of failure.
2. Highly scalable and fault-tolerant with support for data replication.
3. Capable of handling massive amounts of data with fast write operations.

Disadvantages:

1. Lacks support for ACID properties (Atomicity, Consistency, Isolation, Durability).
2. Does not support complex queries or joins like traditional relational databases.
3. Reads can be slower compared to other databases optimized for read-heavy workloads.

Apache Cassandra is well-suited for modern applications requiring continuous availability, high performance, and the ability to handle large-scale data across distributed systems.

Cassandra Use Cases

• IoT sensor data – Massive write ingestion with low latency.
  
• Time-series analytics – Metrics, monitoring, and financial tick data.
  
• Log aggregation – High-ingest logging systems.
  
• Big data analytics – Large-scale data pipelines.
  
• Distributed applications – Systems requiring global uptime.

What is MongoDB?

MongoDB is a highly flexible and scalable NoSQL database that stores data in a document-oriented format. It was developed by 10gen (now MongoDB, Inc.) and first released in 2009 as an open-source project. MongoDB stores data in JSON-like BSON (Binary JSON) documents, allowing for a more flexible and dynamic schema. This makes it ideal for handling semi-structured or unstructured data, suitable for a variety of applications like content management systems and real-time analytics.

MongoDB provides horizontal scalability through sharding, which involves distributing data across multiple servers. This allows for easy scaling out by adding more nodes to the database cluster.

MongoDB implements a strong consistency model, ensuring that all nodes reflect the latest write operations before a write is confirmed. This makes it suitable for applications where immediate data consistency is crucial. MongoDB also supports replica sets, groups of servers that maintain the same data, providing redundancy and high availability. This allows for automatic failover and data recovery capabilities, ensuring continuous operation in case of server failures.

MongoDB uses MongoDB Query Language (MongoQL), which is based on JSON and designed to work seamlessly with BSON documents. It supports complex queries, indexing, aggregation, and other advanced features. MongoDB is optimized for read-heavy workloads, making it suitable for applications requiring fast data retrieval. Its flexible schema design allows for efficient handling of evolving data models and dynamic data structures.

MongoDB is widely used in content management systems, e-commerce applications, real-time analytics, social networks, and mobile applications. It is suitable for applications requiring flexible and dynamic data structures.

Features:

• Type: Document store
  
• Query Language: MongoDB Query Language (JSON-based)
  
• Optimized for: Read-heavy workloads and flexible data models
  
• Consistency Model: Strong consistency by default
  
• Deployment: On-premises, Atlas cloud, or hybrid

Advantages:

1. Open-source, with both community and enterprise versions available.
2. Schema-less design provides flexibility in data modeling.
3. Supports sharding and aggregation, ensuring scalability and performance.
4. Strong consistency model ensures data integrity.
5. Robust security features, including authentication, authorization, and encryption.

Disadvantages:

1. Complex joins are not supported, which can make certain queries more challenging.
2. High memory usage due to its design and indexing capabilities.
3. Limited nesting and document data size compared to some other NoSQL databases.

MongoDB is a versatile and powerful database solution for modern applications requiring flexibility, scalability, and strong consistency. Its document-oriented approach and rich query capabilities make it a popular choice among developers for a wide range of use cases.

Quick Verdict: Cassandra vs MongoDB for Analytics

Bottom Line:

• If you want a tool for write-heavy IoT/logging workloads: Cassandra
  
• If you want a tool for flexible, ad-hoc analytics: MongoDB
  

Cassandra vs. MongoDB

Cassandra vs MongoDB Data Structure for Analytics

Cassandra utilizes a wide-column store data model, where data is organized into tables with rows and columns. Unlike traditional relational databases, each row can have a different set of columns, and you can create columns and tables on the fly. The tabular database relies on the primary key to fetch data, making it somewhat closer to a relational database in terms of data organization.

MongoDB uses a document-oriented data model, storing data in JSON-like BSON (Binary JSON) documents. This allows for a flexible and dynamic schema, where each document can have a different structure, including nested objects. The schema-free nature of MongoDB provides greater flexibility, though a schema can be defined if needed.

Secondary Indexes

Cassandra supports secondary indexes but with some limitations. They are not as powerful or flexible as those in MongoDB and can impact performance. Secondary indexes are useful for queries on non-primary key columns, but their usage should be carefully planned.

MongoDB fully supports secondary indexes, allowing for efficient querying on any field within a document, including nested objects. This enhances query performance and flexibility, making it easier to handle complex queries and optimize read operations.

Query Language

Cassandra uses Cassandra Query Language (CQL), which is similar to SQL. This makes it easier for developers familiar with SQL to transition to Cassandra. CQL is designed to handle the specific needs of Cassandra’s data model and architecture.

//Cassandra (Complex - Requires Spark):
spark.sql("""
    SELECT product, SUM(amount) as total
    FROM sales
    WHERE date >= dateadd(day, -30, current_date)
    GROUP BY product
    ORDER BY total DESC
    LIMIT 10
  """)

MongoDB employs MongoDB Query Language (MongoQL), which is based on JSON. This query language is designed to work seamlessly with MongoDB’s document-oriented structure, allowing for rich and expressive queries on BSON documents. MongoDB can be queried using multiple interfaces such as Mongo shell, PHP, Perl, Python, Node.js, Java, Compass, and Ruby.

//MongoDB (Simple):
  db.sales.aggregate([
    { $match: { date: { $gte: new Date(Date.now() - 30*24*60*60*1000) }}},
    { $group: { _id: "$product", total: { $sum: "$amount" }}},
    { $sort: { total: -1 }},
    { $limit: 10 }
  ])

Scalability

Known for its linear scalability, Cassandra allows the seamless addition of nodes to the cluster to handle increased loads. It distributes data evenly across all nodes in the cluster, ensuring consistent performance as the cluster grows. Cassandra supports multiple master nodes, enhancing write scalability and ensuring high availability by allowing continuous write operations even if some nodes fail.

MongoDB achieves horizontal scalability through sharding, which involves distributing data across multiple servers. While MongoDB primarily uses a single master node with multiple slaves, scalability can be improved through sharding techniques. However, this requires additional setup. MongoDB’s master-slave architecture may lead to a delay of 10-40 seconds for failover during node failure, impacting availability.

Aggregation

Cassandra offers limited support for aggregation operations. It does not have a built-in aggregation framework, and complex queries need to be handled using third-party tools such as Hadoop and Spark.

MongoDB, however, provides a powerful aggregation framework that allows for complex data processing and transformation operations. The aggregation pipeline enables users to perform operations like filtering, grouping, and calculating aggregates directly within the database. However, its built-in aggregation is more efficient for medium traffic, and managing the framework at scale can become complex.

Cassandra vs MongoDB Performance Benchmarks (2025)

Optimized for write-heavy workloads, Cassandra is ideal for applications requiring rapid data ingestion and high write throughput. Its architecture ensures low-latency writes and high availability. User reviews highlight its ability to store large amounts of data, fast data writes, and near-zero downtime. Cassandra is highly regarded for its scalability, open-source nature, and SQL-like CQL.

MongoDB is optimized for read-heavy workloads, providing fast data retrieval and efficient handling of read-intensive operations. Its flexible schema design and support for secondary indexes enhance read performance. User reviews praise MongoDB for its ease of use, flexible document schemas, and robust toolset in cloud environments, though it may incur high costs for small projects.

Real-World Performance Benchmarks: Cassandra vs MongoDB

Reported benchmarks and industry tests reveal clear performance trade-offs between Cassandra and MongoDB for analytics workloads.

Summary : Reported Performance Benchmarks

Disclaimer: These figures are based on third-party benchmarks and field reports, not internal testing. Actual results will vary depending on schema design, indexing strategies, consistency settings, hardware, and workload characteristics.

Licensing

Cassandra is open-source under the Apache License 2.0, allowing free use, modification, and distribution. Enterprise support is available through vendors like Datastax, and it is available on the AWS marketplace.

MongoDB was initially released under the AGPL (Affero General Public License) but has since moved to the Server Side Public License (SSPL). The SSPL requires that anyone offering MongoDB as a service must release the source code of their service. MongoDB is overseen by MongoDB, Inc., and is available on subscription models in different tiers, from basic to advanced, and also available on the AWS marketplace.

Comparison summary: Cassandra vs. MongoDB

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.

However, neither database offers everything that its users desire. That’s where Knowi comes into the picture. Knowi’s end-to-end data analytics capabilities, allows you to natively connect into both these data sources while providing a high-level intuitive UI that allows the users to generate queries and analyze the data with a simple drag and drop functionality. Knowi helps ease the process of data management, data integration and data analysis, helping to process and utilize data more efficiently. Check out the article on MongoDB analytics to learn more. Learn about data integration and analytics on Cassandra Data source in Knowi here.

When to Choose Cassandra

• IoT sensor data – Handles 1M+ writes per second with low latency.
  
• Time-series metrics – Optimized for time-based partitions and sequential writes.
  
• Log aggregation – Scales effortlessly for high-ingest, append-only workloads.
  
• Geographic distribution – Built for multi-region replication and fault tolerance.
  

When to Choose MongoDB

• Flexible analytics requirements – Rich query language and aggregation framework for diverse workloads.
  
• Content management – Stores and queries semi-structured and unstructured content efficiently.
  
• E-commerce catalogs – Supports complex, evolving schemas and product search.
  
• Real-time dashboards – Fast queries with native BI integrations and indexing.
  

When to Use Both

• Cassandra for ingestion, MongoDB for analytics – Leverage Cassandra’s write performance and MongoDB’s aggregation speed.
  
• Unified through Knowi – Connect both data sources directly in Knowi, blend results in real-time, and run analytics without ETL.

 Explore more about Cassandra

  –Cassandra Deep Dive: Complete 2025 Guide (Architecture & Use Cases)

  – Native Cassandra Analytics Tutorial 2025: Real-Time Dashboards & CQL Queries

  – NoSQL Database Deep Dive: Challenges, Use Cases & Key Databases Explained

  –Choosing th Best NoSQL Reporting Tools for Your Team: Here we compare top BI platforms for NoSQL

Frequently Asked Questions