Nowadays, organizations have many millions of users and petabytes of data to handle for their services’ effective rendering. There are enterprise applications on the cloud to deliver dynamic content to different users working on their smartphones, desktops, tablets, and other devices across various locations. There is also a big fundamental shift in business management, which is more digital now. It has helped entrepreneurs in numerous ways.
The paper files of old times are no longer a part of the records system, and we can now store anything and everything on databases now. All organizations are undergoing a massive modernization. All the paper processes and documents, which we used to fax, scan, and store on a legacy mainframe system, is now going to relational databases to process everything electronically.
With modernization, enterprises now need to store all their data in various formats being converted into digital form. As this transformation is happening across every industry now, businesses run differently from what they used to be a decade ago. We are going through a period of change in which all the records are digitally handled. To face this reality of running a business online, organizations are largely looking for scalability, i.e., adding more capacity to accommodate more data and more users. This refers to elasticity for the database systems to scale up and scale back down when there is dissipation in demand.
Scaling relational DBs is tough
The common notion is that achieving elasticity and scalability is a big challenge for relational databases. Built to cater to the needs of an era, relational databases were designed when the use was to keep the data neat, clean, and orderly. However, this is not the truth anymore. All the database vendors are saying that they can scale big, but most of them do it to survive,
You need to take a closer look and see what is working and what may not. The fundamental problem of relational databases with scalability may start to surface as you take that closer look. Relational DBs are meant to run on a single server to maintain integrity and table mappings to avoid distributed computing problems. With this design model, if the systems need to scale, the customers have to buy a more complex and bigger system for which they need to spend more money. Apart from this, they also need to have proprietary hardware with more processing power, storage, and memory. Upgrades may also be a challenge as an organization should go through the lengthy process of acquisition, and it often needs the system to be taken offline to make any changes. All these may happen while the number of users continues to increase, which there by causes much more strain and increased risks on the under-provisioned resources.
Architectural changes hiding the underlying issues
To handle these concerns mentioned above, RDBMS vendors have now come up with a lot of improvements. The development of relational databases nowadays allows us to use multifaceted architectures depending on the master-slave model. The slaves are additional services that can handle replicated data through parallel processing. It is also called shading of data to ease the workload on the master server. Some other enhancements for RDBMS like using shared storage, using replicas better, in-memory processing, distributed caching, and innovative architectures have made the relational databases more scalable. For remote database administration support, avail of the reliable services offered by RemoteDBA.com.
However, beneath these superficial changes, it is not hard for someone to find that the single system and a single point of failure still exist out there. For example, you can see how Oracle has a clustered database that uses a clustered system, but there is still a shared disk subsystem beneath it. These are also accompanied by the high cost, as setting up of single data warehouse may cost millions of dollars. Enhancement of relational databases also comes with many other trade-offs. For example, when the data is distributed across various relational DBs, it is based on predefined queries to maintain performance. In other terms, flexibility is sacrificed for performance. Adding to these, relational DBs are also not designed to scale back down, but they are highly elastic. Once these data have been distributed and more space is allocated, it becomes almost impossible to redistribute the data.
NoSQL databases are to scale
Rather than constraining the limitation of single server database architecture, NoSQL databases are designed to handle massive scaling on distributed database systems. NoSQL DBs can easily scale out horizontally, which means they can run on multiple servers which work together, whereas they share a part of the load as a wall. Using this approach, NoSQL databases can operate across hundreds of services and handle petabytes of enterprise data to manage billions of documents at a time.
Along with these, NoSQL can also manage the process having tens of thousands of transactions in milliseconds. The major catch here is that top NoSQL databases can do all these on their inexpensive commodity hardware, operating in any environment as on-premises or cloud alike. Another big advantage is that if any one of these nodes fails, the others can pick up the workload and eliminate a single-point-of-failure risk. Massive scaling is very impressive, but what is even more important when it comes to elasticity is that not all NoSQL databases are fully elastic. Much unique architecture make it possible to easily and quickly add or remove needed nodes in the cluster so that your enterprise data stays in line with the increased performance needs.
There is also no need for any complex charging for architectural workarounds with the modern NoSQL DBs. Data can be automatically rebalanced among various clusters when the nodes are added or removed. This makes administrators much easier and makes it possible for the DBAs to manage data with lesser headaches.
So, even though NoSQL has many advantages over RDBMS in terms of scalability and elasticity, you need to evaluate your specific use case to make an appropriate choice among these.