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Humanity has always been strongly determined 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 in the midst of a significant shift in the way our world works: data has become the fuel of the 21st century. All areas and sectors depend on it to make decisions.
One thing is certain: the need for data-related skills will continue to increase.
Today, organizations collect raw data from internal and external sources at an unprecedented rate. By analyzing this data, they can use reporting applications, dashboards, and other tools to answer questions and gain valuable insights.
So the right question is how to manage all this data?
SQL remains one of the most in-demand skills for data professionals. Let's explore why this is and how you can join this data revolution.
SQL, the star of data management
SQL, which stands for Structured Query Language, is the standard language for interacting with a database that uses a SQL server. It was created for the purpose of manipulating data sets. It can be used to retrieve, update, delete and create data within a database.
Beyond data manipulation, SQL allows you to alter the structure of the database, such as adding tables, deleting 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 used language among professional programmers.
As most organizations rely on data to make decisions and improve efficiency, SQL is an indispensable skill to maximize the value of data.
Additionally, SQL is one of the core tools in most modern enterprise toolsets, making it a valuable skill even if you are not directly responsible for creating and managing databases.
There are some advantages of learning SQL:
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Managing large amounts of data
SQL is designed to work with big data and can handle complex queries on large data sets much faster than other tools like spreadsheets or even some programming languages like Python. Learning SQL helps you manage and analyze big data effectively.
In many organizations, the core of the data environment is often a data warehouse, where SQL is the primary interaction language.
Interact with other tools
SQL integrates seamlessly with other data science tools and programming languages, such as Python and R. Libraries such as pandas (Python) and dplyr (R) allow you to run SQL queries directly within your code.
This interoperability makes it easy to combine the data management capabilities of SQL with the advanced analysis, visualization, and machine learning capabilities of these languages.
Standardize skills
To query or manipulate data with SQL, statements with keywords such as “SELECT” and “FROM” are used. This SQL syntax is ANSI standardized 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's easy to understand
The basic syntax of SQL is very readable and resembles natural language. Describes 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 anyone can understand the main purpose of the query. It is important to note that while it is not necessary to capitalize these keywords, it is a common convention to do so for readability.
Getting started with SQL
Now that we know that SQL skills are essential for working with data, you may be wondering how to get started. Here's a step-by-step guide to get you started:
Basic SQL Statements: Start with basic SQL statements for retrieving data and manipulating tables.
Aggregate Functions – Learn aggregate functions such as SUM and AVG to summarize data and perform initial analysis on a single table.
JOIN and subqueries: Continue using JOIN and subqueries to combine data from multiple tables.
Once you know the basics, it's important to start doing your own hands-on projects. In the following link you can find some project ideas to do on your own.
Completing these projects will strengthen 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 affecting compatibility and ease of use. For data professionals, learning the differences between SQL dialects like MySQL, PostgreSQL, and SQLite is very beneficial.
Students usually start with SQLite. Capturing the unique characteristics of each dialect can improve code performance and facilitate seamless integration across multiple platforms.
While it is not necessary to be an expert in all SQL dialects, having a basic understanding of the syntax differences is extremely helpful, especially when seeking employment in environments that use different dialects. Many students start with SQLite, but it is advantageous to become familiar with at least one other SQL dialect in addition to SQLite. This knowledge will make you more versatile and better prepared for various data environments.
Soon
- SQL is essential for managing and analyzing large data sets efficiently. Its importance is highlighted by its ranking as the third most used language among professional programmers in 2023.
- SQL integrates seamlessly with other data science tools and programming languages such as Python and R, enhancing its usefulness in data management and analysis across multiple platforms.
- SQL syntax is standardized, making it consistent and easy to learn across different database systems. Its readability and natural language likeness make it accessible to beginners, while knowledge of multiple SQL dialects increases employability and versatility in different data environments.
Joseph Ferrer He is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of data science applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes about all things ai, covering the application of the ongoing explosion in this field.
