What is SQL & Why is it Important to Learn it?

The modern world runs on data. Almost every business, whether an aspiring new startup or a seasoned multinational enterprise, relies on data for everyday operations.

How this data is organised and interacted with comes down to a specific programming language: SQL.

In simple terms, what is SQL?

Structured Query Language, known as SQL or simply “sequel,” is a programming language that communicates through relational databases. It is the easiest way to store, update, remove, search, or retrieve information on a database.

Imagine databases as warehouses, data tables will be your filing cabinets, and the data will be the individual files. Now imagine a warehouse worker who knows the data like the back of their hand and can instantly pull out any file you want access to.

The only thing is that the worker speaks a special language, SQL, which you must learn to communicate with them.

So, it’s easy to learn, right?

Well, it depends. For those with an understanding of programming and knowledge of other programming languages, learning SQL should take a few weeks. For complete beginners, however, it might be a little more difficult.

Still, it’s definitely one of the easiest programming languages to learn. It should certainly be one of the first languages to learn if you’re considering pursuing a career in data science.

What are SQL Standards?

SQL standards are formal guidelines set by the American National Standards Institute (ANSI) that specify the correct use and implementation of Structured Query Language (SQL), which is used for managing and manipulating relational databases.

The standards ensure that SQL code is portable and interoperable across different database systems.

Why do we need SQL?

SQL is used to interact with relational databases. It works by understanding and analysing data of virtually any size, from small datasets to large stacks. It’s a powerful tool that enables you to perform many functions efficiently and quickly.

How it interacts with databases is ‘non-procedural.’ This means SQL’s syntax is very simple, and the coder must only specify “what to do”, not “how to do it.” These interactions are essentially commands, which fall into five categories: data definition, data manipulation, data control, transaction control and data query.

What is a relational database?

A relational database stores and provides access to related data points. It is based on the relational model, an intuitive table data representation.

Imagine you have a box of LEGO blocks, each representing a piece of data. In a relational database, you sort the blocks into different containers based on their colour and size. Relational databases are super helpful for keeping data easy to manage and use.

Why is database management and data retrieval Important?

Database management organises information into a structured format, making it easy and quick to access, much like finding a book in a well-sorted library.

This is essential in environments where speed and efficiency are critical, such as in business operations where fast data retrieval can significantly enhance decision-making and operational agility.

SQL Commands and Core Components

Data definition language (DDL)

Used to update or manipulate a database structure. Commands include CREATE, ALTER, DROP and RENAME.

Data manipulation language (DML)

Enables the modification of a database. Commands include INSERT, UPDATE and DELETE.

Data control language (DCL)

Used to set privilege and permission parameters within the database structure. Commands include GRANT and REVOKE.

Transaction control language (TCL)

Used to manage changes made by DML. It enables these changes to be grouped into logical transactions. Commands include COMMIT, ROLLBACK and SAVEPOINT.

Data query language (DQL)

Used to fetch data from the database. It only uses the SELECT command.

SQL v/s NoSQL

SQL Databases are like the classic file cabinets of the digital world—organised, structured, and great when you know exactly how everything should fit. They use a predefined schema(Tables) to determine the structure of your data before you even start collecting it.

They are ideal for applications where consistency and safety of transactional data are paramount, such as in banking systems. SQL databases are vertically scalable.

On the other hand, NoSQL Databases are the rebels of the database world. They allow you to store data in many different ways-documents, key-value pairs, graphs, or wide columns—depending on your application’s demands.

This flexibility means you can store data as it comes without a rigid structure. NoSQL is fantastic for handling large volumes of diverse and unstructured data that change frequently, like big data and real-time web apps.NoSQL databases are horizontally scalable.

Why should I learn SQL?

First, it’s the cornerstone of almost every modern business. As data is one of the world’s most valuable commodities, manipulating, defining, controlling, and understanding data is crucial. SQL allows you to do all of these things and more.

And the more data-driven your job is, the more important SQL is to succeed.

SQL is also a universal language that can be applied to other disciplines and languages. Learning SQL can help you understand the workings of other languages, such as Python and Java. SQL also makes collaboration easy, as it’s open source and has a large supportive community.

Finally, it’s a prized skill within data science. In fact, SQL is the most in-demand skill among all jobs in the field, appearing in 42.7% of all data science job postings on Indeed.

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