Every programmer, at some point during their career or learning curve, ponders the existential dilemma of whether to use SQL or NoSQL as a database. Which is the superior option? The list of considerations influencing this decision is lengthy, to say the least. Even if you are fortunate enough to find an answer, the next question is which database application to use for your database structure.
Table Of Contents
Let’s take a brief look at the history to get a sense of what’s going on.
In today’s environment, data is the most valuable commodity. Indeed, this has been recognised since the emergence of computers in the latter half of the twentieth century.
In the 1970s, the relational database model was established, and it quickly became popular. The main goal of RDBMS is to eliminate data duplication, which was a huge deal in the 1970s. Data storage was costly, and the infrastructure needed to handle the large surge of data was lacking.
But then the internet (or www, as we like to call it) began to grow at an exponential rate. This generates a massive amount of data. With the proliferation of web applications, the requirement to swiftly create and retrieve massive amounts of data has become even more critical. NoSQL came to the rescue at this point. The expense of data storage or infrastructure was not a concern in the late 2000s.
NoSQL database systems are more prevalent and in use than RDBMS, according to this survey from 2017.
Let’s go through some of the key features and distinctions between SQL and NoSQL.
Querying Language & Database Schema
- SQL: SQL is a structured query language used by SQL databases. They have a pre-defined data structure schema. SQL querying languages have been around for a long time and have progressed significantly, including the addition of excellent libraries to help in querying. It’s ideal for queries and data structures with a lot of complexity. SQL, on the other hand, has a very tight data structure. SQL is a declarative and lightweight query language. Because it is very difficult to change the schema and data structure once a project is implemented, extensive research is required before building and implementing any RDB.
- NoSQL: Because NoSQL has a dynamic schema, data can be created quickly without having to define the structure. Every document has its own structure and syntax, and you can use column-oriented, document-oriented, graph-oriented, or Key-value pairs! Each NoSQL database can have its own query language, adding to the difficulty of learning many languages in order to operate with various databases. The query language used by NoSQL is non-declarative.
SQL: Tables with predefined columns are used to store data in SQL. Every entry is essentially a new row that we are generating or accessing.
NoSQL: Data can be saved in JSON, graphs, key-value pairs, or tables with dynamic columns using NoSQL.
They’re vertically scalable, according to SQL. A single server’s load handling capacity can be improved by adding more RAM, CPU, or SSD capacity. This is also known as scale-up.’
They are horizontally scalable in NoSQL. Sharding allows you to boost data flow by simply adding more servers. They are more scalable and are suited for big amounts of data that change regularly.
Sharding is the technique of dividing huge amounts of data into smaller pieces and distributing them across numerous servers.
This leaves us with lots of options.
- SQL: MySQL, PostgreSQL, Oracle, Microsoft SQL…..
- NoSQL : MongoDB, Apache Cassandra , Google BigTable, Apache HBase…
Making a choice for the right database
For starters, there is never a single best option. There will always be a large number. As they say, to each their own.
But, before we select between SQL and NoSQL, we must first focus on three basic database ideas that are appropriate for our project.
- Structure: Each project requires a unique approach to data storage and retrieval. The structure that involves the least amount of work and is easily scalable should be chosen.
- Speed and Scale: Data modelling can assist in determining the best route to achieve the best speed. Some databases are geared for read-heavy apps, while others are optimised for write-heavy apps. It’s critical to choose the right database for your project’s I/O.
- Size: It is determined by the greatest quantity of data we can store and process before the database is impacted. It can range from a mix of data structures saved to partitioned data spread across various filesystems and servers, as well as vendors particular.
One of the most significant processes for selecting a database and data structure is data modelling. Developers, data architects, and business analysts can examine and understand relationships between data in a database more easily through data modelling. This results in a more organised project structure and fewer complications in the future.
Data modelling is the process of visualising the various forms of data that are used and stored within a system, as well as the relationships between these data types, the various ways that data can be grouped and structured, and its formats and features.
Mixing Database Types
The modelling technique aids us in comprehending project requirements. And in some cases, both types of databases may be used in the same project. When choosing amongst multiple databases, it is critical to choose one that will own a certain set of data, i.e. employ the canonical data model. The data in any database belongs to it, and it can be shared with other databases.
Canonical data models are a type of data model that aims to present data entities and relationships in the simplest possible form in order to integrate processes across various systems and databases.
Understand the data structure required for your project, the amount of data to be stored/retrieved, long-term future needs and scalability, upgrade and maintenance costs, and use a data model to determine whether your database is relational, document, columnar, key-value pair, graph, or a combination of both.
It is also beneficial to read the documentation of a few well-known databases.