How to Actually Get Started with SQL

A clear (and human) guide to get started without drowning

Many of you have been asking how to get started in the data world. I know it can seem complex and intimidating, but fear often clouds our vision.

That’s why I want to remind you all that SQL is still the number one data language and the easiest one to learn.

If you’re looking to break into this field, there’s not better advice than…

START

LEARNING

SQL

RIGHT

NOW!!

Still not sure? Let’s give some reasons why 👇🏻

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.

And guess what… all of this is just DATA!

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?

And the answer is quite simple: SQL remains the best option to do so.

You can go check the latest StackOverflow Developer Survey 2025, where SQL stands as the 3rd most used programming language of all developers!

(The first one if we only consider those used in the data science field 🥇)

So let’s try to understand better SQL’s advantages 💥

SQL’s main advantages

#1. It can deal 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 analyze 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.

#2. It can be easily integrated 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, visualization and ML features of these languages.

#3. It is standardized

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.

#4. 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.

So after all of this information, you might be wondering…

How to (ACTUALLY) get started with SQL?

This roadmap will help you to get started, build a strong foundation, and progress through more advanced topics.

Let’s break down each of the previous steps!

1. Learning the Basics

Before diving deep, it’s essential to grasp what is SQL. Begin with:

• What is a Relational Database?

• Comparing SQL and NoSQL Databases

• Understanding SQL Syntax and Basic Commands

2. Exploring Data Types

Once you have a clear image of SQL, you should understand the core data types you will be working with 👇🏻

• Integers, Decimals, and Booleans

• Character Types: CHAR and VARCHAR

• Dates and Binary Data

3. Mastering Basic Keywords

The first real step is to familiarize yourself with the commands that form the backbone of SQL querying:

• SELECT, FROM, WHERE

• ORDER BY and LIMIT Clauses

You will see that SQL queries are usually quite easy to understand, as their syntax resembles natural language. The most common SQL query you will find is.

Already proficient with basic commands?

Now it’s turn to understand how to manage databases with the following two steps:

4. Data Definition

Learn how to structure and manage your databases with:

• CREATE, ALTER, DROP Statements

5. Data Manipulation

Get hands-on with inserting, updating, and deleting data:

• INSERT, UPDATE, DELETE Commands

Now you should keep getting better with SQL programming language 👇🏻

6. Understanding Aggregation

Aggregate functions are powerful tools for summarizing data:

• SUM, AVG, COUNT, MAX, MIN

This will be one of the most important steps when creating KPIs and specific metrics.

7. Enforcing Constraints

Ensure data integrity through constraints:

• PRIMARY KEY, FOREIGN KEY, UNIQUE

• NOT NULL and CHECK Constraints

Our data will usually be spread across many different tables, this is why being able to merge data from different sources is crucial in SQL.

8. Mastering JOINs

JOINs are critical for combining data from multiple tables:

• INNER, LEFT, RIGHT, and FULL OUTER JOINs

Your SQL skills here are already really good!! 🙌🏻 But learning must go on…

9. Leveraging Subqueries

As queries keep getting more complex, you need to create subqueries.

• Nested and Correlated Subqueries

• EXISTS and NOT EXISTS

Now you should understand advanced syntax like specific functions and new ways of merging data like UNION.

10. Advanced Syntax

Explore more complex SQL operations:

• UNION, INTERSECT, EXCEPT

• CASE, COALESCE, and Functions for NULL Handling

Once a result is good, you might want to create a view!

11. Working with Views

Views can simplify complex queries:

• Creating, Modifying, and Dropping Views

Now you should focus on crafting better queries by leveraging indexes and following best security practices 💥

12. Optimizing with Indexes

Indexes improve query performance:

• Unique, Clustered, and Non-Clustered Indexes

13. Data Integrity and Security

Now that you are here, I strongly recommend you get familiar with security best practices. Maintain the security and integrity of your database:

• Using GRANT and REVOKE

• Database Security Best Practices

The two final steps is learning how to craft better and more optimized SQL queries. You know I am a big fan of modular code with CTEs, so that’s something we will talk more about in coming issues! 😉

14. Performance Optimization

Techniques to enhance the efficiency of your queries:

• Query Analysis, Optimizing JOINS, Reducing Subqueries

15. Advanced SQL Techniques

For seasoned users, explore sophisticated SQL features:

• Windows Functions, Recursive Queries

• Pivot/Unpivot Operations, Common Table Expressions (CTE)

And this is all for today!

We will see in more detail most of the previous steps, this is just a summary to help you get started with SQL!

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How to Get Started

1. Master the basics: SELECT → FROM → WHERE, then ORDER BY, GROUP BY/HAVING, and core JOINs (INNER, LEFT).

2. Set up your stack: Use SQLite/DuckDB locally or a warehouse (BigQuery/Snowflake), with a comfy editor (DB UI or Jupyter + DuckDB); add a simple BI view if helpful.

3. Ship tiny projects: KPI query pack (WAU/MAU, conversion, revenue by channel), a cohort/retention table, and a customer 360 (users + orders → first/last order, AOV, LTV).

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A final note

If you’ve made it this far, you’ve already done the hardest part: deciding to start.
SQL rewards short, consistent reps.

Ship one tiny query this week, then another next week.
A KPI today, a JOIN tomorrow, a view next Friday.

Momentum compounds.

Are you in?

— Josep

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Still with me? 🧐

As fellow data enthusiasts, I’m sure you’d be eager to help me shape some impactful KPIs and take this newsletter to the next level!

So here’s how you can help:

👉🏻 I want this newsletter to be truly valuable for you, so please share your feedback!

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Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support—it really helps and means a lot!

P.S. Share this with the coworker who “doesn’t need SQL” and spends lunch VLOOKUP-ing.

Any doubt? Let’s start a conversation! 👇🏻