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Humankind has always been strongly shaped by its ability to store and share information. Studies indicate that a key distinction between humans and other animals lies in our ability to create, preserve, and inherit knowledge and culture across generations.
Today we are amid a significant shift in how our world works: Data has become the fuel of the XXI century. All fields and sectors rely on it to make decisions.
One thing is certain: The need for data-related skills will only keep surging.
Organisations today gather raw data from both internal and external sources at an unprecedented rate. By analysing this data, they can use reporting applications, dashboards, and other tools to answer questions and gain valuable insights.
So the right question to be done is how to manage all this data?
SQL remains one of the most demanded skills for data professionals. Let’s explore why this is the case and how you can join this data revolution.
SQL, the Star of Data Management
SQL, which stands for Structured Queries Language, is the standard language for interacting with a database that uses an SQL server. It was created for the purpose of manipulating sets of data. It can be used to retrieve, update, delete, and create data within a database.
Beyond data manipulation, SQL allows you to alter the database structure, such as adding tables, delete records and setting access permissions.
Since its creation in the 1970s, SQL has become the standard language for data analysis. According to Stack Overflow’s 2023 survey,
SQL ranks as the third most commonly used language among professional programmers.
As most organisations rely on data to make decisions and improve its efficiency, SQL is an indispensable skill for maximising data value.
Furthermore, SQL is one of the core tools in most modern business toolsets, making it a valuable skill even if you’re not directly responsible for creating and managing databases.
There are some advantages of learning SQL:
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Dealing with big amounts of Data
SQL is designed to work with big data and can handle complex queries on large datasets much faster than other tools like spreadsheets or even some programming languages like Python. Learning SQL helps you manage and analyse big data effectively.
In many organizations, the core of the data environment is typically a data warehouse, where SQL is the primary language for interaction.
Interacting with other Tools
SQL integrates seamlessly with other data science tools and programming languages, such as Python and R. Libraries like pandas (Python) and dplyr (R) allow you to run SQL queries directly within your code.
This interoperability makes it easier to combine SQL’s data handling capabilities with the advanced analytics, visualisation and machine learning features of these languages.
Standardize Skill
To query or manipulate data with SQL, you use statements with keywords such as “SELECT” and “FROM.” This SQL syntax is standardized by ANSI and ISO-certified, ensuring consistency across the hundreds of databases and data tools that support SQL today.
While some databases and tools may extend the syntax with specialized operators, commands, or functions, the fundamental principles of SQL remain consistent.
Once you master the basics of SQL, you can apply this knowledge universally across different platforms.
It is easy to understand
Basic SQL syntax is highly readable, resembling natural language. It outlines how data should be retrieved or manipulated.
Consider the following example query:
SELECT first_name, last_name, date_of_hire
FROM employees
WHERE date_of_hire > '2018-12-31'
ORDER BY date_of_hire, last_name;
In this query, the SQL keywords SELECT, FROM, WHERE, and ORDER BY define the actions to be performed and any person can understand the main purpose of the query. It is important to consider that while these keywords don’t need to be capitalised, it’s a common convention to do so for better readability.
Getting Started with SQL
Now that we know SQL skills are essential for working with data, you might wonder how to begin. Here’s a step-by-step guide to get you started:
Basic SQL Statements: Start with the basic SQL statements to retrieve data and manipulate tables.
Aggregate Functions: Learn aggregate functions like SUM and AVG to summarise data and perform initial analyses on a single table.
JOINs and Subqueries: Move on to using JOINs and subqueries to combine data from multiple tables.
Once you know the basics, it is important to start doing your own hands-on projects. In the following link you can find some ideas of projects to do on your own.
Performing these projects will reinforce your understanding and prepare you for practical data tasks.
Differences Between SQL Dialects
SQL dialects are variations of the SQL language tailored to different database systems, each impacting compatibility and ease of use. For data professionals, learning the differences between SQL dialects such as MySQL, PostgreSQL, and SQLite is highly beneficial.
Learners usually start with SQLite. Grasping the unique features of each dialect can enhance code performance and facilitate seamless integration across various platforms.
While it’s not necessary to be an expert in every SQL dialect, having a basic understanding of the syntax differences is extremely helpful, especially when seeking employment in environments that use different dialects. Many learners start with SQLite, but it’s advantageous to familiarize yourself with at least one other SQL dialect beyond SQLite. This knowledge will make you more versatile and better prepared for diverse data environments.
In Brief
- SQL is essential for handling and analyzing large datasets efficiently. Its importance is highlighted by its ranking as the third most commonly used language among professional programmers in 2023.
- SQL integrates seamlessly with other data science tools and programming languages like Python and R, enhancing its utility in data management and analysis across various platforms.
- SQL’s syntax is standardized, making it consistent and easy to learn across different database systems. Its readability and natural language resemblance make it accessible for beginners, while knowledge of multiple SQL dialects increases employability and versatility in different data environments.
Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics engineering and is currently working in the data science field applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes on all things AI, covering the application of the ongoing explosion in the field.
