How to Decide Between Relational Database and NoSQL Databases?

Once we decide to develop an application that is going to deal with a database, a big question will come to our mind; should we go with a relation database or a NoSQL database?

Both databases differ based on the following key points.

Relational and nonrelational behaviors.
Based on schema, whether it is predefined or dynamic.
How they will scale up in the future.
The data that we store in the database will fit for multirow transactions or unstructured data.

Before going to take this topic ahead let’s take a basic understanding of both databases.

What is SQL or Relational database?

SQL generally falls under a relational database because it is based on a relational model. This relational model was introduced by Edger F. Codd in the 1970’s.

The relational model defines a methodology for organizing data in a structured and relational manner. Here, structured means organizing data in terms of tables with columns and rows, and relational means defining relations between these tables in terms of primary key and foreign key, or we can say referential integrity.

A relational database is still widely used for querying databases, whereas data is stored in rows and tables that are linked in various ways. One table record may link to one other or to many others, or many table records may be related to many records in another table in these relational databases.

These relational databases offer fast data storage and data recovery. It can handle great amounts of data and complex SQL queries.

What is NoSQL or a Non-Relational database?

NoSQL means non-relational databases. Such databases came into existence in the late 1960s, Here we are using NoSQL it doesn’t mean this database system doesn’t use SQL. NoSQL is sometimes defined as “Not Only SQL”.

NoSQL systems allow us to work with different data structures within a database because it uses a dynamic schema for unstructured data.

NoSQL databases are not relational, so it doesn’t store data in rows and tables. Instead, it generally falls into one of four types of structures.

Key-value databases: These databases store data as key-value pairs and are optimized for simple and fast read/write operations. It associates unique identifiers with specific data blobs, making it possible to store just about any type of data. Examples – Memcached, Redis, Coherence.
Document databases: In such databases, Data is stored as documents in a format such as JSON or XML, etc, and each document is assigned its own unique key and can be queried using document-oriented query languages. Examples – MongoDB, CouchDB, Cloudant.
Column-oriented databases: Column-oriented, where data is stored in cells grouped in a virtually unlimited number of columns rather than rows. Making it possible to query and aggregate large volumes of data very quickly. These types of databases are called by other names as well, such as column-store, wide-column store, and column-family. Examples – HBase, Big Table, Cloudera.
Graph databases: In such types of databases, Data is stored in graph structures that define how the data is interconnected. These databases store data as nodes and edges and are designed to handle complex relationships between data. Examples – Amazon Neptune, Neo4j.

SQL vs NoSQL

The decision of which type of database to use - SQL or NoSQL - will depend on the particular needs and requirements of the project. This decision will depend mostly on the workloads you are going to plan to support and the structure and amount of data.

For example, if you need a fast, scalable, and reliable database for web applications, then a NoSQL system may be preferable. On the other hand, if your application requires complex data queries and transactional support, then an SQL system may be the better choice.

Some other things are also considerable, such as maturity, stability, licensing fees, vendor support, and the extent and participation of the developer communities.

Consider Relational Databases	Consider NoSQL Databases
We can go with this database if our data is highly structured and this structure is not going to change frequently.	If we are working with large amounts of unstructured or semi-structured data, that will not fit the relational model.
If you are going to develop an accounting and financial application in such an application, a transaction-oriented system is highly required, so we must go with a relational database.	If you are going to work or develop a social media application or such application here, a transaction-oriented system is not required; we can opt for a NoSQL database.
If you are looking for a high degree of data integrity and security, we should go with a Relational or SQL database.	If your application requires the flexibility of a dynamic schema or wants more choice over the data model, in that case, we can go with the NoSQL database.
In your application, having routinely performed complex queries, including ad hoc requests in that case we must opt Relational database.	If you are looking for a database system that can be scaled horizontally, perhaps across multiple geographic locations, you can go with NoSQL.
Your application doesn’t require the scale-out capabilities that NoSQL offers.	Your applications don’t require the level of data integrity offered by Relational databases.
Relational Database makes it suitable for data warehousing and online analytical processing. It’s ACID-compliant, which means transactions are atomic, consistent, isolated, and durable. This makes Relational Databases useful when you work with complex transactions, like in eCommerce, transactional, and financial use cases.	If you want to streamline development and avoid the overhead of a more structured approach and your application allows this kind of volitation, then you can go with the NoSQL database.

Conclusion

Choosing the right database in system design requires careful consideration of the technical requirements of the application, as well as strategic factors such as cost, maintainability, and team expertise. By understanding the pros and cons of SQL and NoSQL, as well as other database options, candidates can make an informed decision on which technology to use.