Can NoSQL Big Data Applications meet the Challenges of Your Big Data Applications: You can see how Big Data is becoming bigger and bigger and chaotic lately. In light of the features that the internet brings forth and the surge of social media and mobile technologies, a massive volume of varied, unstructured data is being generated daily. Live data streaming is happening at an unprecedented speed, and it is bombarding the modern-day businesses of all sizes. This data explosion is really huge and too complex for the relational database management systems to handle. Fortunately, for those business organizations that want to reap the best benefits out of their big data banks, a new breed of functional databases can effectively tackle the big data challenge, i.e., the NoSQL DBs.
It was relational databases like Microsoft SQL Server, Oracle, and MySQL, etc. enjoyed the monopoly in the enterprise database management sector. However, this scenario is now rapidly changing. Over the last half a decade, NoSQL databases are gaining popularity, and database platforms like Apache Cassandra, MongoDB, and HBase are enjoying exponential growth in their usage and functionality compared to their relational DB counterparts.
However, this unprecedented grown and adoption of the NoSQL DBS does not mark the demise of traditional data warehousing. However, it does mark the fact that many organizations are now turning towards NoSQL as a more decentralized and cloud-friendly database solution to meet big data platforms’ needs. If your organization also intends to achieve more with big data, then here we will have a detailed look at both NoSQL and RDBMS to help you choose the apt database solution. For those who a newly the database administration process and confused about deciding terms of choosing an appropriate database for your enterprise applications, it is advisable to assist a skilled professional consulting service in terms of remote database designing, implementation, and administration.
NoSQL vs. RDBMS comparison
Fixed and flexible schema
Choosing between RDBMS and NoSQL is largely based on the needs of your business data storage and processing. For example, if your organization’s primary focus is on business intelligence and reports based on the data, which required an in-depth analysis of structured data, then relational DBMS fits you the best. The primary reason for this is that the RDBMS tends to operate within fixed schema designs, in which each of the tables is defined strictly by a collection of uniform rows and columns. Relationships can be easily established with this model between each row within a table or with another table. So, relational databases are well suited for transactional applications, which are complex by offering atomicity, stability, and data integrity.
On the other hand, NoSQL is a wonderful functional choice for businesses that want to handle their workloads more tuned toward in-line processing and analysis of the huge volume of varied unstructured data for Big Data. Unlike RDBMS, the NoSQL databases do not have any restrictions as having a fixed schema model. Usually, NoSQL DBs apply the schema to read than write. This will further the NoSQL DBs more suited for the high-volume and wide variety of online applications. Equipped with NoSQL technology, the businesses can become more agile and flexible in storage, retrieval, and processing a huge volume of structured and unstructured data. For a better decision making regarding your database choice and for ongoing remote support database administration, you may get the assistance of RemoteDBA.com.
In terms of fathering performance reports and conducting core analytics, the complex transactional applications may need to handle a vast volume of structured data. With this need, relational databases can offer more atomicity, stability, and data integrity than NoSQL solutions. However, relational DBs are not built with the need for scalability and agility, which is also necessary to meet the challenges that modern Big Data applications raise. Also, the RDBMS cannot take advantage of inexpensive storage and offer the processing power readily available.
NoSQL is built in response to the voluminous and rapid rise in unstructured data and the latest processing challenges in data analytics. NoSQL incorporates solutions for a wide range of database technology challenges. The latest NoSQL solutions can offer various conventional RDBMS products like high performance, availability, and scalability for big data applications. The organization that looks for the needs to store massive data and processes the same in real-time, NoSQL can help store structured, semi-structured, and unstructured data files and enable real-time processing of the same.
A typical RDBMS has a monolithic architecture and can only scale vertically with the addition of more hardware. As the data volume increases, more servers needed to be added to accommodate the same. While trying to spread the RDBMS over many servers, it becomes costly and becomes a more time-consuming process that required extra engineering to ensure data integrity.
NoSQL databases can offer a more efficient architecture that can scale horizontally too. There is a scope of increased storage and computational ability by simply adding more commodity servers or through cloud instances. Along with it, the open-source of many NoSQL DBs makes it much more cost-efficient for the enterprises than the traditional DBs.
To conclude, we can see that the explosion of big data is now causing enterprises of all sizes to find out better ways to store, manage, process, and analyze the huge volume of unstructured data. Over the relational databases, the new NoSQL solutions offer many benefits and prove to be a very powerful solution for the organizations looking forward to joining the big data bandwagon.
For organizations with a higher functional need, combining RDBMS and NoSQL’s benefits can be a very effective approach. As with any other new technology, the organizational leaders looking to adopt NoSQL solutions may have to exercise thorough due diligence. They have to weigh all the pros and cons of these against their actual business objectives. Every business is different and needless to say its requirements also vary. So an accurate analysis will go a long way in helping you make the best selection and work efficiently.