Cassandra vs DynamoDB
Updated on 20th Jun, 23 9.2K Views

We will go into deep detail about Cassandra and DynamoDB in this article. Most people believe that these two technologies’ functionality is nearly identical. Finally, we will examine the advantages and disadvantages of Cassandra and DynamoDB.

In this blog, we are going to describe the following points given below:

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Cassandra vs DynamoDB

In most cases, the data store you select depends on the issue you’re trying to resolve. Incredible scale and availability are offered by both Cassandra and DynamoDB.

The similarities between the two end there; they can both handle tens of millions of reads and writes, they can both provide some resilience in the event of failure, and they both use the same underlying architecture.

To have a better idea we would like to comprehend the following subjects, let’s talk about these technologies in more detail.

Cassandra

An open-source NoSQL database with enormous scalability, Apache Cassandra is perfect for managing vast amounts of structured, semi-structured, and unstructured data across numerous distant locations.

The foundation of Cassandra is a log-structured merge tree, a very effective data structure for high-volume write operations. Keeping time series data is Cassandra’s most common use case.

With no single point of failure and continuous availability across a large number of commodity servers, Cassandra also offers linear scalability, operational simplicity, and a potent dynamic data architecture built for maximum adaptability and quick reaction times.

Data is spread throughout all cluster nodes in Cassandra, a masterless peer-to-peer distributed system. Each node understands the cluster’s topology and communicates with other nodes in the cluster regularly.

Instances/machines are used by Cassandra, which can be configured to use native and OS-level page caching. As a result, hot partitions are frequently served from memory, and if necessary, the real power of a single machine (and its replicas) can be devoted to performing a single partition.

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DynamoDB

A fully managed NoSQL database service called DynamoDB provides speedy and dependable performance along with seamless scaling. Solid-state discs (SSDs) are used to store all of the data items in DynamoDB.

To provide built-in high availability and data durability, the data is automatically replicated across three facilities in an AWS region.

With Amazon DynamoDB, you can pay a low variable fee for just the resources you use while offloading the administrative burden of running and scaling a highly available distributed database cluster.

Users can build databases using DynamoDB that can handle any volume of traffic and be able to store and retrieve any quantity of data.

To manage each customer’s demands dynamically and to maintain quick performance, it automatically distributes data and traffic across servers.

The scalability and flexibility of DynamoDB are its two fundamental benefits. Users are able to deal with practically anything while maintaining consistency because it does not need the use of a certain data source and structure.

DynamoDB was created with the intention of completing all requests in under one millisecond.

DynamoDB integrates the essential system attributes in a special way that benefits users and application developers.

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Differences between Cassandra and DynamoDB

Before we look into the pros and cons of these technologies we should try to understand the basic differences between Cassandra and DynamoDB. By reading ahead, we can eliminate the misconception that both technologies are functionally equivalent.

CassandraDynamoDB
Cassandra is implemented as a wide-column store.DynamoDB is a pure key-value store.
Cassandra supports full-commit log backups.DynamoDB only supports snapshot-style backups.
Cassandra allows the user to fully customise every component of the data replication and is highly tunable.  The number of replicas used cannot be controlled by DynamoDB because AWS handles everything automatically.  
Cassandra typically provides significantly lower latency.DynamoDB provides significantly higher latency.
A NoSQL database with extremely high scalability and availability is called Apache Cassandra.DynamoDB is a NoSQL cloud database service that provides consistent performance, without foundation.

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Cassandra Advantages and Disadvantages

Cassandra has numerous advantages where it excels and also lacks in some of the specific areas, let us know the following areas which are given below:

Advantages of Cassandra

Other NoSQL and relational databases do not have access to some specific advantages that Apache Cassandra does, let’s see the advantages that exist for this database.

Large-scale storage:

Cassandra expands to hundreds of terabytes while operating on cost-effective clusters that offer top performance.

Management simplicity:

Cassandra clusters are simple to scale and can be dynamically resized between clouds as needs evolve.

Continuous availability:

Cassandra has enabled zero-downtime upgrades for more than ten years and is designed to be “always on.”

Applications that require a lot of writing:

Cassandra excels in write-intensive applications like time-series streaming data, sensor log data, and Internet of Things applications.

Analytics and statistics:

Distributed analytic systems like Spark use Cassandra as data storage. The DataStax Spark Cassandra Connector enables users to take advantage of Spark’s robust in-memory analytical operators.

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Disadvantages of Cassandra

We have seen the areas where it shows advantages, now take a look at where this database lack:

No Aggregations functions

SUM, MIN, MAX, AVG, and other aggregations are extremely resource-intensive, if even possible, to perform in Cassandra because it is a key-value store.

No Adhoc Queries

Cassandra data are hidden beneath the covers.

A key-value storage system is basically what the storage layer is.

In other words, developing a data model depends more on the queries that will be run, than it does on the actual data structure.

Unpredictable Performance

The performance of Cassandra can be unpredictable because it has numerous asynchronous processes and background tasks that are not scheduled by the user.

As a result, performance effects that might not be connected to a question or the volume of searches might be observed. This can make resolving performance problems challenging.

Nodes Communication Problem

Due to the protocol’s inability to move data between the nodes, there is a lack of communication between the nodes.

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DynamoDB Advantages and Disadvantages

Even though DynamoDB offers those unique features, we must be aware of both its benefits and drawbacks before implementing it in our applications. So let’s talk about DynamoDB’s benefits and drawbacks to have a better understanding.

Advantages of DynamoDB

Fast, reliable performance, and smooth scalability is features of the fully managed NoSQL database service Amazon DynamoDB. The advantages of using Amazon DynamoDB include:

Zero Administration Overhead

There is zero administrative overhead because Amazon DynamoDB handles the responsibilities of hardware provisioning, setup and configuration, software and hardware updates, monitoring, and handling hardware failures.

Unlimited Throughput and Scale

With Amazon DynamoDB’s provisioned throughput approach, you may set the throughput capacity required to handle almost every volume of request traffic.

The quantity of data that can be stored and retrieved using Amazon DynamoDB is essentially limitless.

Elasticity and Flexibility

With consistent latency and no latency rise or throughput reduction as the data volume expands with increased usage, Amazon DynamoDB can tackle uncertain workloads with predictable performance.

Integration with other AWS services

Amazon DynamoDB includes integration with other AWS services, including logging and monitoring, security, analytics, as well as others.

Disadvantages of DynamoDB

Low latency

If you need to store more than 64KB per item, Dynamo is an expensive and incredibly low latency option.

Consistency comes with high-cost

Strongly consistent read operations, which demand more work and use twice as many database resources than ultimately consistent reads, are used to calculate read capacity units.

Limited Queries

Data querying has very few uses, especially if you wish to query data that is not indexed.

No support for atomic transactions

There is atomicity in every write action to an object. Any attribute changes made by a write operation are either all correctly updated or none are updated.

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Summing up

We hope that this blog helped you understand Cassandra and DynamoDB better, the two databases. Cassandra is the best choice if open source is in need. DynamoDB is advised if you plan to use AWS products frequently. However, regardless of which database is the best, we should always keep in mind how both technologies will affect the organization’s performance, flexibility, and resource pool.

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