The rise of data and databases has led businesses to shift their thinking from relational databases to NoSQL databases. The data volume is rising, organizations are relying more on data-driven insights and hence the hitches associated with relational DBMS are being felt. NoSQL database solutions are now being preferred, thanks to their competence in handling large volumes of data without needing a logical schema or category, with ease. Two popular names in this arena are Cassandra and MongoDB, both of which are NoSQL databases, with their own set of advantages attached.
NoSQL databases like Cassandra and MongoDB use data structures like graphs, key-value, wide column, and document stores. They can easily handle unstructured, semi-structured, and structure data. These databases facilitate developers to be fast and agile, in terms of handling code updates. There is high-end scalability and reliability based on modern-day data requirements.
In this article, we shall be comparing the two NoSQL stalwarts – Cassandra and MongoDB. Before we start their comparison, let us individually read through their overview, features, and organizations using them.
What Is Cassandra?
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
Originally developed by Avinash Lakshman and Prashant Malik at Facebook, Cassandra is now a key part of the Apache Software Foundation. It is an open-source and free NoSQL distributed database system, that manages large data volumes through nodes, via a columnar storage architecture. The nodes here are competent in doing read and write operations and hence, data can replicate across multiple nodes. In case of a node failure, the user can move to the nearest node with the necessary data. It has high data availability, low failure rates, real-time analysis, and its Cassandra Query Language is quite effective, just like SQL.
- Simple to maintain, easy to scale, and fast to operate
- Automatic data balancing
- Fault-tolerant and consistent database system
- Easy data distribution
- Use of masterless ring architecture
- Offers advanced repair processes for read, write, and data consistency
- Real-time sensor data and messaging system
Companies Using Cassandra:
What Is MongoDB?
MongoDB is a general-purpose, document-based, distributed database built for modern application developers and the cloud era. It is used by millions of developers to power the world’s most innovative products and services. It has the competence to serve multiple Fortune 500 and global 500 organizations across various industry segments like healthcare, education, eCommerce, financial, etc.
Good Read: What Is MongoDB? Understand In 200 Words
Key Features Of MongoDB:
- Horizontal scaling, distributed storage, and high availability
- Offers replication, support for several storage engines
- Schemaless database, faster query handling through indexes
- Reduced I/O overload and dynamic schema for easy data structures
- Flexibility, real-time view of data, nested object structure
- Indexable array attributes, on-desk encryption in the enterprise version
Companies Using MongoDB:
- The New York Times
MongoDB And Cassandra Similarities:
There are certain similar characteristics that apply to both these NoSQL databases – Cassandra and MongoDB. These features make them both highly popular and competitive. Here are some of them.
Both Cassandra and MongoDB are:
- NoSQL databases and can store large amounts of data without needing schema or logical category
- Free, open-source, and downloadable at no extra cost. Setting them up is easy and free.
- Supporting sharding horizontal partitioning
- Compatible with Windows, Linux, and macOS
- Not replaceable to traditional RDBMS database types
- Not compatible with normalization and consistency
Cassandra vs MongoDB: A Comprehensive Comparison
|Characteristics||High performance distributed database system, majorly designed to handle huge amount of data from multiple commodity servers||Cross-platform document-oriented database system, designed to access applications faster and easier|
|Written In||Java||C++, Go, Python, etc.|
|Developed by||Apache Software Foundation in July 2008||MongoDB Inc. in February 2009|
|Licensed||By Apache||By AGPL and drivers by Apache|
|Architecture||Utilizes a wide column store, distributed architecture, making it available||Depends on a document store, master-slave architecture with less fault tolerance|
|Support for Indexes||Does not completely support secondary indexes||Supports secondary indexes for getting data|
|Query Language||Has its own query language (CQL)||Supports third-party languages like Java, Python, etc.|
|Aggregation||Depends upon third party tools for aggregation||Has an in-built framework for aggregation|
|Handling Failure Situations||Offers high availability with almost no point of failure||Easy to administer just in case of any point of failure|
|Scalability for Writing||Quite high and efficient||Limited scalability in writing|
|Server operating systems||Linux, OS X, Windows, BSD||Solaris, Linux, OS X, Windows|
|Read Performance||Very efficient as it takes less time||Not that fast read performance|
|Replication Method||Uses Selectable Replication Factor method||Uses Master Slave Replication method|
|Data Storage and Usage||Uses columns and tables for data storage like SQL format||Store data in JSON like documents|
|Data Availability||Utilizes multiple masters inside a cluster instead of a single model||Utilizes a single master directing multiple slave nodes|
|Database Schema||A stationery database schema, facilitating static typing||A flexible arrangement not needing a schema, hence more adaptable|
|Data Model||Traditional data model with rows, table structure, and columns||Rich, expressive, object-oriented data model|
|ACID transactions||Does not offer ACID transactions but can be tuned to support||Offers multi-document ACID transactions with snapshot isolation|
|Use cases||eCommerce, real-time analytics, fraud detection, online courses, music catalogs, data streaming, sensor data, messaging systems||eCommerce, real-time analytics, mobile, Internet of Things, content management systems, operational intelligence, product data management|
|Analysis||Best choice when users have structured or unstructured data with an expectation of faster growth of the database||Best choice when users have data without a transparent definition of the data structure|
|In-memory Competencies||Does not possess in-memory capabilities||Does possess in-memory capabilities|
|Third-party Products and Services||CData, DataStax Enterprise, Instaclustr||Fivetran, ClusterControl, Datadog, CData|
|Support||Comes from third-party companies like Impetus, Datastax, etc.||Enterprise-grade support all the time with extended lifecycle support|
|Active Community Support||Apache software foundation offers a community site with a detailed support system||MongoDB community support offers details about events, webinars, etc.|
|Pricing Models||Cassandra is free for all users except for the data warehouse||MongoDB has different pricing models based on user needs|
On A Parting Note:
To each its own! Though there are differences between Cassandra and MongoDB – the two leading NoSQL databases, both have their quote of popularity and loyalty. Organizations must evaluate their own factors in-depth before choosing which one to take. In a world ruled by databases, BI, and Big Data solutions, it is a tough choice to choose from. But this is where the advancement of technology comes into the picture! Choosing the better out the best!