The three-letter abbreviation for SQL, pronounced “es-q-el” or “si-quel”, is a pretty important skill to learn. SQL stands for Structured Query Language. It’s not a programming language, at least not quite. It is a “query language” that retrieves information from a database.
When we need to choose a DBMS, we most often prefer relational SQL, bypassing non-relational (NoSQL) structures. However, both options have advantages. Let’s take a look at their key features.
How to understand the difference between SQL and NoSQL?
Let’s imagine that we have a city A, where all the inhabitants speak the same language, and this language is the only means of interaction between people. There is also city B, where all the inhabitants speak different languages. At the same time, they interact with the outside world in completely different ways, that is, there is simply no universal means of communication.
These simple examples clearly demonstrate the main differences between relational and non-relational databases.
Yes, relational databases use SQL to define and process data, a flexible query language whose choice minimizes the number of risks and is great for complex queries. But there is a downside: SQL has a number of limitations. In particular, building queries obliges you to predefine the data structure, that is, if you later want to change this structure, this can be disastrous for the system.
As for non-relational databases, they offer a dynamic data structure, that is, data can be stored in different ways: columnar, document-oriented, based on key-value pairs, or in the form of graphs.
What such flexibility can give in practice:
- the ability to create documents without having to set their structure in advance;
- each document is able to have its own structure;
- each database may have its own syntax;
- it is possible to add fields directly in the process of working with data.
Most often, SQL databases scale vertically, that is, you can increase the load on a single server and increase the power of central processors, as well as the amount of RAM or storage systems. In the case of NoSQL, databases scale horizontally. This means, for example, that you can increase traffic by distributing it or by adding more servers to your DBMS. It’s like adding more floors to a building, or even adding more buildings to a street. That is, in the case of NoSQL, the system can become both larger and more powerful, which should be remembered if a database is needed for large or constantly changing data structures.
In SQL systems, data is represented in the format of tables, while in NoSQL it is in the format of documents, graphs, key-value pairs, or wide-column storages. This state of affairs makes SQL database the best choice for applications involving multi-entry transactions (same account system) or for legacy systems previously designed and built for relational structures.
Finally, let’s recall the main DBMS of both types:
SQL: MySQL, Oracle, PostgreSQL, Microsoft SQL Server.
NoSQL: MongoDB, Redis, RavenDB Cassandra, BigTable, HBase, Neo4j, CouchDB.
Why is it important to know SQL and NoSQL?
These tools are used by almost all major technology companies. Uber, Netflix, Airbnb – the list goes on. Even at companies like Facebook, Google, and Amazon, data science teams use SQL to query data and perform analysis.
Today, almost every vacancy related to programming requires the candidate to know SQL and NoSQL. In particular, specialties such as data analyst, data scientist, database administrator, back-end developer, product managers, mobile application developers, and many others. So if you want to start a career in IT, take the time to learn these important technologies. They will definitely come in handy at work!