Apache Cassandra vs MongoDB: A Comprehensive Analysis

The volume of data collected by most companies in the world today has increased exponentially in recent years. This is simply due to the fact that companies rely more than ever on data-driven decision-making. Given the volume of data being collected now, it is impossible for the traditional relational database to continue to meet those data storage requirements. This is mainly due to the inability of relational databases to scale out and handle unstructured data.

Therefore, most of today’s companies that handle large volumes of data are

moving to NoSQL database solutions that are designed to handle large amounts of data keeping in mind the Big Data requirements of most companies. Some of the most popular NoSQL databases are MongoDB, Apache Cassandra, Oracle NoSQL Database, Apache HBase, etc. There are several factors that can help you decide which NoSQL database would be best for your business and data requirements.

This article will provide you with an in-depth understanding of the various factors driving the Apache Cassandra vs MongoDB decision, allowing you to understand which would be right for your business.

Table of Contents

Introduction to Apache Cassandra Introduction to MongoDB Key factors driving Apache Cassandra vs MongoDB Decision:Apache Cassandra vs MongoDB: Apache Cassandra vs MongoDB data model: Availability Apache Cassandra vs MongoDB: Scalability Apache Cassandra vs MongoDB: Apache Cassandra vs MongoDB query language:

    Apache Cassandra vs MongoDB aggregations:

    • Secondary index

Apache Cassandra vs MongoDB: Support for Apache Cassandra vs MongoDB programming languages: Pricing conclusion Introduction to Apache Cassandra

Image source: https://commons.wikimedia.org/wiki/File:Cassandra_logo.svg

Apache Cassandra

is a free and open source NoSQL database. It implements a columnar storage architecture and can handle large volumes of data distributed across multiple Apache Cassandra nodes. Each node in Apache Cassandra is capable of both read and write operations. Because of this, data can be replicated across multiple nodes to provide availability in the event of node failure. If a node failure occurs, the user is redirected to the nearest available node that has the necessary data. Therefore, it can be observed that Apache Cassandra does not have a single point of failure and can therefore provide high data availability. This is considered one of the most significant advantages of using Apache Cassandra.

Another advantage of using Apache Cassandra is its query language. It uses Cassandra Query Language (CQL) to access data that has a syntax very similar to Structured Query Language (SQL). Because of its similarity to SQL, most developers can easily switch to Apache Cassandra.

More information about Apache Cassandra can be found here.

Introduction to

MongoDB

Image Source: https://www.mongodb.com/brand-resources

MongoDB is a leading open source NoSQL database. MongoDB stores data in a JSON-like form, i.e. as key-value pairs in a document. Each document is considered part of a collection. MongoDB is a NoSQL database that offers distributed storage, scale-out, and high availability.

More information about MongoDB can be found

here. Key Factors Driving Apache Cassandra vs MongoDB Decision The various factors driving the Apache Cassandra vs MongoDB decision are as follows: Apache Cassandra vs MongoDB: Data Model Apache Cassandra vs MongoDB: Availability Apache Cassandra vs MongoDB: Scalability Apache Cassandra vs MongoDB: Apache Cassandra vs MongoDB

Query

  • Language

  • :
  • Aggregations
  • Apache Cassandra vs MongoDB: Secondary index Apache Cassandra vs MongoDB: Support for Apache Cassandra vs MongoDB programming languages: Pricing 1) Apache Cassandra

  • vs MongoDB: Data Model
  • Apache
  • Cassandra

implements a columnar storage architecture and stores data in the form of traditional rows and columns. Each column has a specific data type that it can store and that must be specified at the time of table creation. An example table in Apache Cassandra is as follows

:

Image source: https://www.slideshare.net/yellow7/cassandralesson-datamodelandcql3

MongoDB, on the other hand, stores data in a JSON-like format in documents that are stored in a collection. This means that the structure of the data to be stored can be changed for each record and does not require it to be in a predefined format. Storage in a JSON-like format also allows data to be nested to allow the record to be more data-rich and expressive. An example document in MongoDB is as follows

:

Image source: https://www.mongodb.com/what-is-mongodb

So if the data

you’re trying to store has a fixed format that isn’t expected to change much, Apache Cassandra would be right for you, but if your requirements include more dynamic data that doesn’t have a predefined structure, MongoDB would be more suitable. 2) Apache Cassandra vs MongoDB:

Availability

MongoDB has a single master node that controls multiple slave nodes. If the master node goes down, an automatic election process begins at the end of which one of the slave nodes is chosen to become the master node. This process can take up to a minute to complete, and the database would not respond to any requests in the absence of a master node. Therefore, although MongoDB has high availability, it cannot guarantee 100% data availability.

Apache Cassandra, on the other hand, has multiple master nodes within a cluster. This means that if one of the master nodes goes down, there is no downtime as other active master nodes can handle incoming requests. Because of this architecture, Apache Cassandra can guarantee 100% availability for writes.

So, if

your business and data requirements need 100% data availability, Apache Cassandra would be more suitable, and if a small amount of downtime can be tolerated without major repercussions, MongoDB would be suitable.

3) Apache Cassandra vs MongoDB: Scalability

Distributed databases only allow the master node to perform write operations and slave nodes only perform read operations

.

Since MongoDB has only one master node, it can only perform one write operation at a time and can therefore be considered limited in terms of write scalability. On the other hand, Apache Cassandra that has multiple master nodes can coordinate multiple write operations at the same time.

Therefore, if write scalability is an important factor for your business, Apache Cassandra should be preferred.

4) Apache Cassandra vs MongoDB: Query Language Apache Cassandra supports a query language called Cassandra

Query Language (CQL), while MongoDB has no support for any query language and can only structure queries in JSON fragments.

An example query for inserting a record into an Apache Cassandra table is as follows:

INSERT INTO employee (empid, first name, last name, gender) VALUES (‘1’, ‘FN’, ‘LN’, ‘M’) The same query in MongoDB

will have an implementation as follows

: db.employee.insert( { empid: ‘1’, first name: ‘FN’, last name: ‘LN’, gender: ‘M’ } ) If support for a query language is required,

Apache Cassandra should be preferred over MongoDB. The Apache Cassandra CQL also has a structure very similar to Structured Query Language (SQL). So if your company has a team that already masters SQL, Apache Cassandra would be the best choice for you.

5) Apache Cassandra vs MongoDB:

Aggregations MongoDB

has its own built-in aggregation framework that

allows users to run an ETL pipeline that can perform the required aggregations on the data

.

Apache Cassandra does not have a built-in aggregation framework. If data stored in Apache Cassandra must be added, external tools such as Apache Hadoop or Apache Spark are required.

So, depending on the qualifications of the engineering team in your business, you can choose Apache Cassandra or MongoDB.

6) Apache Cassandra vs MongoDB: Secondary index MongoDB

is known for its offering of high-quality secondary indexes. Because of its flexible data model coupled with its high-quality secondary indexes, MongoDB can get any value from the stored object, even if it is nested.

Apache Cassandra only offers cursor support for secondary indexes that are limited to individual columns and equality operations.

Therefore, the choice between the two depends on how you plan to query the data. If the required data can be accessed using a single primary key, Apache Cassandra would be suitable, but if more complex queries are required to extract specific values in dynamic data, MongoDB should be preferred.

7) Apache Cassandra vs MongoDB: Support for programming languages The programming languages supported by

MongoDB are Actionscript, C, C#, C++, Clojure, Erlang, Go, Groovy, Haskell, Java, JavaScript, Lisp, Lua, MatLab, Perl, PHP, PowerShell, Ruby, Scala, Smalltalk, ColdFusion, D, Dart, Delphi, Prolog, Python, R. The programming languages supported by

Apache Cassandra are

C#, Erlang, Go, Haskell, Java, JavaScript, Perl, Ruby, Scala, C++, Clojure, PHP, Python

. Although Apache Cassandra

supports a comparatively smaller number of programming languages, the final decision depends on the programming languages in which your company’s applications are written or will be written. 8) Apache Cassandra

vs MongoDB: Pricing Apache Cassandra

is free for all users. Users only have to pay for the Data Warehouse that will be used to store the data. Therefore, the final price of Apache Cassandra depends on the data storage solution the company uses.

MongoDB offers 3 pricing plans based on business requirements. These plans are as follows:

Cloud Database as a Service On-premises or Private Cloud Solutions MongoDB Realm Cloud Cloud Database as a Service This plan offers

  • a cloud
  • data storage

solution fully managed by MongoDB.

In addition, it offers 3 levels which are as follows

:

  • Shared clusters: these are mainly used for learning purposes and are not suitable for businesses. This tier offers 512 MB of free storage after which the user has to start paying.
  • Dedicated clusters: mainly used by companies that offer services only in a specific region.
  • Multi-region dedicated clusters: Used by companies offering services in multiple regions around the world.

The price for each of these tiers is as follows

: Image source

: https://www.mongodb.com/pricing

On-premises

or private cloud solutions

This plan is offered for those companies that do not want to use MongoDB’s cloud offerings and want to use their private cloud or their own on-premises solution for data storage. MongoDB does not offer a transparent pricing model for this plan. The final price can be determined based on your business and data needs after having a discussion with the MongoDB sales team.

Image source: https://www.mongodb.com/pricing

MongoDB

Realm

A plan offered for those businesses that only plan to use MongoDB for Android, iOS, or web apps. MongoDB Realm allows you to build applications faster using edge-to-cloud synchronization and also offers fully managed backend services like Triggers, Functions, GraphQL, etc. The price of MongoDB Realm is as follows

: Image source

: https://www.mongodb.com/pricing

More details on MongoDB pricing can be found here

.

Conclusion

This article provided you with a complete comparison of the various features offered by Apache Cassandra and MongoDB that allows you to make the right choice based on your business and data requirements

.

Most companies today have their data stored in multiple databases. If any analysis is to be carried out, the data from all these sources must first be integrated. Companies can choose to create their own in-house data integration solutions that would require a large amount of investment or use existing platforms such as Hevo.

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