Data is everywhere, but how do you draw insights from it? Often, structured data is stored in relational databases, meaning collections of related tables of data. For instance, a company might store customer purchases in one table, customer demographics in another, and suppliers in a third table. These tables can then be joined together and queried. To interact with relational databases in this way, you’ll likely use the SQL language to retrieve data, join tables, and sort and filter the results.
In this article, we look at what SQL is, which topics are best to focus on based on your chosen job role, and how to approach your learning path.
What is SQL?
SQL stands for “Structured Query Language.” It is a popular computer language used to interact with relational databases.
As an example of basic SQL usage, let’s suppose you keep a spreadsheet of how much you spend per month with a credit card. You might want to know in which months during the last year you spent at least $100. A SQL query to answer this question might look like this:
SELECT months
FROM credit_card
WHERE expense >= 100 and date > "2025-01-01"The code sounds almost like an English sentence: “Select the months from the credit card table where the expense is at least 100 and the date is after January 1, 2025.”
Why should you learn SQL?
SQL is ubiquitous. As this article points out, SQL is used across industries (marketing, finance, healthcare, and so on) and job functions (business analytics, data science, web development). Understanding data at a deep level is a required skill for many high-paying data jobs, including data analyst, data scientist, and data engineer roles. In the data world, SQL is a common tool to retrieve data, modify it, and make queries.
AI Tools and Learning SQL
This brings up questions: In the age of vibe coding and Copilot, why bother learning SQL? Won’t SQL skills become obsolete in the near future? There are many counter-arguments to this point of view.
First, SQL is fundamental to our way of understanding, evaluating, and debugging existing queries. Even if someone/something else writes a particular piece of code, you need to know SQL to understand how a query works. It’s difficult to see how someone can evaluate existing code without understanding its underlying language.
Second, how do you judge if the LLM- generated code is giving you legitimate answers or not? If its answers are incorrect, would the query be correct with simple modifications, or is the generated query nonsense?
Third, there is a huge amount of legacy code in existence that needs to be maintained. It seems unlikely that it will all get replaced with LLM-generated content.
Do Data Jobs Really Use SQL?
Are SQL skills useful for existing data jobs? Yes, many jobs require SQL skills, usually in conjunction with other skill sets.
For instance, a data scientist might pull a subset of data from a database using SQL, then clean the data using Python, then use that data for predictive modeling using Python and an ML library, such as scikit-learn or XGBoost. Often, the insights needed from the data can be found by filtering and subsetting the data, which can all be accomplished using only SQL.
A data engineer might use SQL for ETL (extract, load, transform). This involves retrieving data from sources, transforming it into a suitable format for downstream analysis,and loading transformed data into a data warehouse. A job such as this is typically quite heavy on SQL.
Job Listings
Let’s take a look at some LinkedIn job listings that require SQL data skills. Here’s a page of some positions that appeared when I searched for “SQL Data Engineer” (September 2025).
Salary ranges for this search varied quite a bit. For instance, in the listing for a Data Engineer (L5) at Netflix, the base pay range is $170,000/yr to $720,000/yr whereas an ad for a Junior Data Engineer at another company listed a base pay range of $64,000/yr to $70,000/yr. So what sort of salary is realistic? As of this writing, according to datanerd.tech, of the nearly 170 thousand relevant jobs analyzed, the average yearly pay for a job that involves SQL is $130,000 per year.
Note that skills don’t live in isolation. Many roles require a collection of skills. So for instance, a data scientist job might require SQL and Python or R expertise. A data engineering job might ask for skills in addition to SQL, such as building data pipelines, or cloud technologies, such as MS Azure.
How to learn SQL
So, let’s suppose you’ve decided that learning SQL is a good idea. How should you go about learning SQL? First, before diving in, get organized. Choose an environment, learn the basics, make a toy project, then choose an appropriate learning path based on your interests and professional needs to continue your SQL journey.
Choose an environment
There are many Database Management Systems (DBMS) available. A DBMS is the software you use to interact with a database. Three popular open source DBMSs are PostgreSQL, MySQL, and SQLite. The SQL syntax might vary slightly between the systems, but all three are suitable for learning SQL. The knowledge you learn in one DBMS is directly applicable to others, and minor syntax differences are easily resolved by checking the documentation. Another option is to use a DBMS in a cloud service such as Amazon AWS or MS Azure.
Learn the basics
A common mistake is to try to memorize the syntax of SQL. This approach is not a good use of your time, as you’ll end up trying to memorize an encyclopedia’s worth of information. Instead, there are many resources from which you can look up syntax when you need it. Over time, you’ll remember common syntax from use and practice.
First, look at the syntax and structure of relational databases.
- What are SELECT statements?
- How do you filter data with WHERE clauses?
- What are CRUD operations (create, read, update, delete)? How do you use them?
It’s best to learn by interacting with the data, not just passively reading books or watching endless streams of YouTube videos. DataCamp offers many interactive courses, including one on SQL fundamentals. They also offer a SQL Associate Certification.
Learn SQL in context, not syntax
Once you’re comfortable retrieving data fields using SELECT, and filtering the data with WHERE clauses, you’re ready to experiment with a toy project.
One useful and manageable project is to analyze your personal finances. First, get the data. You might already have your finances in a spreadsheet. If not, use a spreadsheet to create the data. Make a small (twenty or thirty rows) dataset of your finances. Columns might include the amount spent, the card or bank account you used, the type of purchase (e.g., utilities, bills, entertainment, transportation, food), date.
Save this data (or your own) as a .csv file. Now, import this .csv into your DBMS as a table called `credit_card`. The details vary from one DBMS to another, but each has a mechanism to import .csv files into tables. For instance, if you’re working in SQL Server, the instructions are here.
Once you’ve saved the data, use SQL to query the data using SELECT and WHERE clauses. How much have you spent after January? You could write a query similar to the following.
SELECT date, amount
FROM credit_card
WHERE date > 'Jan-25'Now, create new queries. How much do you spend on food per month on average? How much for dining out? In a dataset this small, you could figure this out without using a database, but the point here is to learn how to use the technology. After you’ve asked these questions, you can expand the dataset and write more queries. What other questions could you answer? Spend a week or two on this project.
Learning Paths
The breadth and depth of SQL you initially need to focus on depends on your interests and professional goals. This video suggests three sample paths (beginner, intermediate, advanced). Let’s look at these paths and then map them to common data roles.
The beginner path consists of learning these SQL skills:
- How to use the SELECT statement to retrieve fields from a database
- How to use a WHERE clause to filter data
- How to sort data using the ORDER BY clause
- How to combine tables using JOIN statements
- How to aggregate data using SUM, COUNT, AVG, GROUP BY, and the like
- How to create nested queries (“subqueries”)
The intermediate path adds these skills to the beginning path.
- How to use Common Table Expressions (CTE) to create temporary results used by a larger query
- How to optimize queries using indexing strategies
- How to automate database tasks with stored procedures and triggers
- How to design better databases via techniques such as normalization, denormalization, and schema design
- How to use window functions for advanced aggregation
To be an advanced SQL user, also learn the following skills.
- How to prevent SQL injection attacks
- How to use advanced joins in complex queries
- How to optimize queries for big data
- How to pivot data from a row-based format (commonly used by relational databases) to a column-based format (commonly used by “NoSQL” databases such as MongoDB)
Which path is appropriate based on your professional interests?
- Beginner: If you have no experience with SQL, start at the beginning. This path is appropriate for novices wanting to dip their toe into the data world, or for people who are curious about data but don’t need to delve too deeply into the mechanics, such as project managers who want to run simple queries for themselves.
- Intermediate: If you already use some SQL but need to advance your knowledge, perhaps for a promotion or a career change, this might be an appropriate path. Junior to mid-level data analysts and data scientists need to understand data at this level. You’ll probably want to take at least two or three weeks per topic.
- Advanced: Those who need a deep knowledge of data infrastructure, such as data engineers, need to understand SQL at a deep level, so this is an appropriate track for them.
Closing Thoughts
Although SQL can be a large, sprawling language with many corners and nuances, at its heart, it is a comparatively easy to learn computer language, based on simple English statements.
Good luck on your SQL learning journey! One way to get started learning SQL is through DataCamp’s SQL Fundamentals course. It’s an interesting, powerful language to know.
