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Cassandra Vs SQL

Cassandra Vs SQL

May 20th, 2019

Cassandra Vs SQL

What is Cassandra?

Cassandra powered by Apache is a type of open source and distributed or decentralized storage system. It manages structured data in bulk, which spread out across different regions of the world. Cassandra storage system thus provides an excellent level of service without any room/scope for flaw or failure in its functions. 

Why Cassandra?  

Until now, leading companies have used Apache Cassandra based on its ability to offer the following benefits-

Open Source Project

Cassandra is an open source project powered by Apache. Hence, you may easily download and use it according to your individual choice. Furthermore, you may integrate Cassandra with various other Apache powered open source projects, including the Apache Hive, Apache Pig and Hadoop apps without any complication.

Features Elastic Scalability  

Cassandra features elastic scalability because of which you may easily scale-up and/or scale-down its cluster in a hassle-free way. In fact, you may add or delete ‘n’ numbers of nodes in Apache Cassandra with less possible disturbance. Because of scaling, both read and write throughput increase at the same time with zero downtime or any other pause to the already existing application.

Supports Peer-to-Peer Architecture

Cassandra provides support to peer-to-peer architecture to add large numbers of nodes or servers to its cluster and in almost every data center. As each of the machines remains at an equal level, each server may entertain request/requests from the clients. Thus, based on outstanding features and robust architecture, Cassandra has become an outstanding database storage system as compared to its counterparts.

Facilitates Data Replication

Data replication is a striking feature possessed by Apache Cassandra, which makes it fault tolerant and highly available. Replication implies that you can store each data at multiple locations. A prime benefit, in this case, is that even when a particular node fails, the user may retrieve the lost data easily from any other location. Hence, replication of each row in a cluster of Cassandra takes place in accordance with the row key. In this way, you may set multiple numbers of replicas of your own choice and thereby, obtain a higher level of recovery and backup competencies.

Supports Tunable Consistency

Cassandra supports tunable consistency. Accordingly, you can adopt either of two types of consistency to choose i.e. the Strong Consistency and Eventual Consistency depending on your specific requirements. Eventual consistency gives approval to the client once the cluster accepts any write. Strong consistency on the other side indicates broadcasting of an update to different nodes or machines responsible to hold particular data. Even you may blend both strong and eventual consistency. For instance, you may choose an eventual consistency for remote data centers to deal with high latency and strong consistency for local data centers with low latency.

What is SQL? 

SQL i.e. Structured Query Language refers to a domain-specific language and it manages the relational databases, while does different operations on the respective stored data. SQL is thus a standard database language among all other relational database management systems. These include SQL Server, Oracle, MySQL, Sybase, and MS Access.

Why SQL?

Structured Query Language gives plenty of benefits to software engineers, which include the following-

Does Not Require Coding and Offers Portability

A standard SQL lets you manage the entire database system without any requirement of writing a substantial amount of complicated codes. In addition, SQL is useful in programs operating in servers, PCs, laptops and a few of the mobile phones.

Interactive Language

SQL is an interactive language and hence, it is useful to communicate with databases and to obtain the answers of even the most complicated questions within only a few seconds.

Multiple Data Views

By using a Structured Query Language, users may create different views of a single database structure and different databases to meet the requirement of different users.

Data Integration and Analytical Queries

SQL is useful to write various data integration scripts and to set up as well as run analytical queries on a regular basis.

Difference between Cassandra and SQL 




Structured Vs Unstructured Data

Cassandra is responsible to deal with unstructured types of data.

SQL as a type of RDMS only deals with structured types of data.

Scope to Handle Data

Cassandra handles all sorts of data simultaneously. Accordingly, it deals with images, videos, sound and many more.

SQL covers a particular type of data only. Accordingly, it handles data consisting of characters, texts, and numbers.

Volume of Data

Cassandra is able to handle data in bulk or a high volume of data that too in a simultaneous way.

SQL handles data of moderate volume and that too at any particular instant.

Table and  Dimension

The table in case of Cassandra implies a storage unit i.e. a list containing key-value pairs in nested form. Here, column constitutes a specific storage unit, while row represents the replication unit for different nodes.

The table in SQL constitutes a storage unit i.e. an array of large numbers of arrays. Here, column highlights the essential attributes of any relation and row represents an individual record.

Resemblance with Trees

Storage models of Cassandra resemble LSMs i.e. Log Structured Merge trees in a broad way.

Storage models of SQL are mainly reminiscent of hash-tress or traditional b-trees.

Facilitates Improved Real-time Performance

With Cassandra, you will expect to achieve improved real-time performance, as it supports the writing of data multiple times to allow the retrieval process efficiently.

SQL does not support the writing of data more than one time and hence, it results in a compromise in the real-time performance.

Ability to Handle High Storage

Cassandra is able to handle high storage.

SQL is able to handle moderate storage.

Data Transfer and Data Storage

As Cassandra facilitates automatic distribution of data, it allows relatively fast data transfer to or from the storage.

As SQL allows manual distribution of data, the speed of data transfer, in this case, is relatively slow than of Cassandra.

Type of Schema

Cassandra possesses flexible schema (also referred to as schema-less) and hence, it is of highly scalable.

SQL features a fixed schema and hence, it indicates limitations in the database storage.

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