Data persistence has always been a key challenge faced by software developers and programmers. Several database management systems have been introduced since the beginning of electronic computing in order to handle data persistence issues in software products. Relational database management systems, powered by SQL, have ruled the IT industry for more than four decades, though the advent of NoSQL database has triggered a new debate regarding whether to choose SQL or NoSQL databases.
Why NoSQL Databases were Introduced?
It is important to mention here that SQL-based RDBMS are highly structured, in which data is stored in the form of well-organized tables with associations among them. This data is queried using a structured query language. There are, however, certain limitations with this approach. Today, the magnitude of data that needs to be handled is enormous. Also, data coming from different sources is versatile. Therefore, conventional SQL-based DBMS are not suited to handle this enormous and versatile data. To combat this, NoSQL database management systems were introduced which address the aforementioned concerns.
NoSQL or SQL – Factors Affecting the Decision
While deciding whether to choose NoSQL or SQL based DBMS for a particular project, the following are some of the considerations that should be taken into account.
1 – Type of Data
Choosing a database depends majorly on the type of data that your project needs to store. If your data is highly structured and associations among the program entities are clearly defined (for instance, if you are developing a point of sale system where you need to store customer orders and product records), conventional SQL based databases are the best fit.
On the flip side, data from molecular modeling, Geo-spatial information, and satellite data are highly unstructured. Likewise, data from social media analysis and websites is also highly unstructured, and relationships among the data entities are not clearly defined. In such scenarios, NoSQL is a better choice. For example, a data mining application should utilize the power of NoSQL database rather than conventional SQL.
2 – Database Volatility
Software development is an agile process where requirements can quickly change, which affects the database schema as well. It is almost impossible to correctly implement the database schema in the first shot. If persistent data of the project is more likely to change in the future, NoSQL databases are a better option since they don’t have any rigid scheme which makes them more suitable for such projects.
3 – Time and Cost
Time is crucial in the software development life cycle. In the past, companies hired dedicated database administrators, while software developers mainly focused on application development aspects. However, this decoupling of DBA and software developers resulted in increased software development time and cost as well. NoSQL technologies such as JSON allow software developers to integrate data and development perspective, leading to cost-effective and timely delivery of software projects.
4 – Scalability
Scalability is one of the significant issues with SQL based databases. With the huge magnitude of information needed to be stored, data size grows exponentially. SQL-based databases undergo vertical scaling, which is extremely costly. On the other hand, NoSQL DBs scale horizontally and scalability issues can easily be handled by adding another node in a database cluster. Google’s HDFS scaling systems is one example.
5 – Data Mining and Machine Learning Perspective
Data mining and machine learning are processes of analyzing data in order to extract useful information and patterns which be used for the decision-making process. These techniques are usually applied over enormous and extremely versatile data. Therefore, in such projects, NoSQL databases are a better choice.
What to choose: NoSQL or SQL?
Having studied the factors that affect the decision, the answer can easily be found. If the project is expected to see drastic changes, needs to handle the huge and versatile amount of data, or the database entities and scheme is ambiguous at the start, go for NoSQL. However, if the project needs to handle small and homogeneous data, and the databases entities are clearly defined with unambiguous relationships (which rarely is the case), SQL is a good fit.
