The Abilities of Relational Databases in light of the Rise of NoSQL DBs


In modern-day enterprise computing systems, there is an enormous amount of data that comes in every day from the web and other touchpoints. A large amount of this data is still handled by the traditional relational database management systems (RDBMS). In the relational data model, all these data come from shared data banks, making it easier to program the applications. Beyond the primary objective of data storage and retrieval, the relational DBMS model is also highly suited for client-server applications, making it a very strong technology for business and web-based applications.

 

Relational databases follow ACID rules

 

In an ideal database management situation, the database transaction needs to be Atomic, Consistent, Isolated, and Durable. This is called the ACID properties of a database. Let’s discuss it in more detail below.

 

  • Atomic: In the atomic model, a transaction is identified as a logical work unit that needs to be changed as a single unit or none of them being performed.

 

  • Consistent: At the end of each transaction, all data must be left in a consistent state.

 

  • Isolated: Any modifications of data that a transaction needs to be performed should be independent of other transactions. Any modifications of data performed by a given transaction must also be independent of other transactions. If it does not happen this way, then the outcome of such a transaction will be erroneous.

 

  • Durable: When a given transaction is completed, then the effects of the data modification performed by that transaction must remain permanent in that system.

 

Distributed database systems

 

A distributed system comprises multiple servers and various software components which will communicate through a network. There are different possible configurations in a standard distributed system like mainframes, personal computers, workstations, servers, and so on. A distributed system’s components interact with each other components and share common resources to achieve the desired goals.

 

Advantages in distributed computing

 

More fault tolerance (reliability)

 

The most important benefit of distributed computing is reliability. If a set of machines in the distributed systems crash, then the rest of the computers remain unaffected and ensure the system as a whole doesn’t fail.

 

Scalability

 

A distributed system can easily expand by adding more machines as needed. This makes distributed systems highly scalable and cost-effective as you need to pay for only as much as you use.

 

Sharing resources

 

In the modern-day enterprise application environment, shared data becomes essential for smooth applications like banking, finance, healthcare, etc. As the data and resources are shared in a distributed system, it becomes easier to ensure any availability and instant processing of data.

 

Flexibility

 

As distributed computing is much flexible, it is easy to install it part by part, implement, and new services instantly and cost-effectively.

 

Speed and performance

 

Distributed computing systems possess greater computing power, and its processing speed makes it different from the other database systems. Speed of performance is a much-needed factor when it comes to the success of enterprise applications. You can consult RemoteDBA experts for any queries or support.

 

Open system

 

As distributed systems as open, all the services are equally accessible to all the clients, either local or remote. This makes it possible to use enterprise systems when the organization spans across different geographical locations.

 

Better performance

 

The wider collection of processor nodes in the distributed system will offer better performance than the centralized systems.

 

Disadvantages of distributed database systems

 

Software  support

 

When it comes to availing support for the distributed system software, you may find that there is only limited support, which one of the major disadvantages of the distributed computing systems.

 

Networking issues

 

As the distributed systems largely rely on external networks, external network service providers’ infrastructure-related issues may cause transmission problems, data overloading, or loss of messages, etc. Sometimes, timely database communication may get interrupted due to network failures, which may affect the application-based operations’ smooth run.

 

Security issues

 

As anyone can get easy access to distributed systems may pose some security risks also. However, there are strict security measures, authentications, and password protection to be implemented to counteract this risk effectively.

 

Scalability

 

In terms of electronics like hardware and communication infrastructure, distributed systems may have limited scalability if you fail to expand the systems to meet the business needs. For example, scaling out a web application is all about getting more people to use it. You may scale a system by just upgrading the hardware without changing the application much, but by adding extra hardware. There are two modes for scaling as:

 

  1. Vertical scaling

 

This is to scale out vertically using adding resources within the same unit to increase its capacity. Adding a CPU to the existing server is an example. You may also expand the memory and storage by the addition of hard drives.

 

  1. Horizontal scaling

 

The scaling process means adding more nodes to the distributed system, like adding new computers. In the NoSQL distributed systems, the data may be much faster as these can take advantage of the “scaling out” options, which means adding more nodes to the existing system to distribute the load over to those nodes.

 

We were discussing the advantages or disadvantages of distributed systems as above. NoSQL or SQL is a model of non-relational DBMS, which is different from the conventional RDBMS systems in many ways. NoSQL is specifically designed for the distributed data stores where large-scale data storage needs can be met. These data storage models do not require a fixed schema, avoid any join operations, and scale horizontally with ease.

 

As of late, data becomes much easier to access and capture through various channels, including social media, among others. You may collect and store various types of user information, including personal data, social graphics, geo locations, log data, and so on. The volume of data is going up exponentially, and it comes in various structures and formats. To take the leverage of big data and analytics, you need to process these data in huge amounts, for which SQL databases may not be competent. On the other hand, the evolution of NoSQL DBs helps to handle this exploding data volume much effectively.

 


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