Data is all over the place, but how do you draw insights from it? Often, structured data is stored in , meaning collections of related tables of knowledge. For example, an organization might store customer purchases in a single table, customer demographics in one other, and suppliers in a 3rd table. These tables can then be joined together and queried. To interact with relational databases in this manner, you’ll likely use the SQL language to retrieve data, join tables, and kind and filter the outcomes.
In this text, we have a look at what SQL is, which topics are best to deal with based in your chosen job role, and easy methods to approach your learning path.
What’s SQL?
SQL stands for “Structured Query Language.” It’s a preferred computer language used to interact with relational databases.
For example of basic SQL usage, let’s suppose you retain a spreadsheet of how much you spend per thirty days with a bank card. It is advisable to know during which months throughout the last yr you spent not less than $100. A SQL query to reply this query might appear 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 bank card table where the expense is not less than 100 and the date is after January 1, 2025.”
Why do you have to learn SQL?
SQL is ubiquitous. As this text points out, SQL is used across industries (marketing, finance, healthcare, and so forth) and job functions (business analytics, data science, web development). Understanding data at a deep level is a required skill for a lot of high-paying data jobs, including data analyst, data scientist, and data engineer roles. In the information world, SQL is a typical tool to retrieve data, modify it, and make queries.
AI Tools and Learning SQL
This brings up questions: Within the age of vibe coding and Copilot, why trouble learning SQL? Won’t SQL skills turn into obsolete within the near future? There are a lot of counter-arguments to this perspective.
First, SQL is prime to our way of understanding, evaluating, and debugging existing queries. Even when someone/something else writes a specific piece of code, you might want to know SQL to know how a question 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 providing you with legitimate answers or not? If its answers are incorrect, would the query be correct with easy modifications, or is the generated query nonsense?
Third, there is a large amount of legacy code in existence that should be maintained. It seems unlikely that it’ll all get replaced with LLM-generated content.
Do Data Jobs Really Use SQL?
Are SQL skills useful for existing data jobs? Yes, many roles require SQL skills, normally along side other skill sets.
For example, a knowledge scientist might pull a subset of knowledge from a database using SQL, then clean the information using Python, then use that data for predictive modeling using Python and an ML library, equivalent to scikit-learn or XGBoost. Often, the insights needed from the information might be found by filtering and subsetting the information, which might all be completed using only SQL.
A knowledge engineer might use SQL for ETL (extract, load, transform). This involves retrieving data from sources, transforming it into an appropriate format for downstream evaluation,and loading transformed data into a knowledge warehouse. A job equivalent to this is often quite heavy on SQL.
Job Listings
Let’s take a have a look at some LinkedIn job listings that require SQL data skills. Here’s a page of some positions that appeared once I looked for “SQL Data Engineer” (September 2025).
Salary ranges for this search varied quite a bit. For example, within the listing for a Data Engineer (L5) at Netflix, the bottom pay range is $170,000/yr to $720,000/yr whereas an ad for a Junior Data Engineer at one other company listed a base pay range of $64,000/yr to $70,000/yr. So what form of salary is realistic? As of this writing, in accordance with datanerd.tech, of the nearly 170 thousand relevant jobs analyzed, the typical yearly pay for a job that involves SQL is $130,000 per yr.
Note that skills don’t live in isolation. Many roles require a set of skills. So for example, a knowledge scientist job might require SQL and Python or R expertise. A knowledge engineering job might ask for skills along with SQL, equivalent to constructing data pipelines, or cloud technologies, equivalent to MS Azure.
Find out how to learn SQL
So, let’s suppose you’ve decided that learning SQL is a great idea. How do you have to go about learning SQL? First, before diving in, get organized. Select an environment, learn the fundamentals, make a toy project, then select an appropriate learning path based in your interests and skilled must proceed your SQL journey.
Select an environment
There are a lot of (DBMS) available. A DBMS is the software you utilize to interact with a database. Three popular open source DBMSs are PostgreSQL, MySQL, and SQLite. The SQL syntax might vary barely between the systems, but all three are suitable for learning SQL. The knowledge you learn in a single DBMS is directly applicable to others, and minor syntax differences are easily resolved by checking the documentation. An alternative choice is to make use of a DBMS in a cloud service equivalent to Amazon AWS or MS Azure.
Learn the fundamentals
A standard mistake is to attempt to memorize the syntax of SQL. This approach just isn’t a great use of your time, as you’ll find yourself attempting to memorize an encyclopedia’s price of data. As a substitute, there are numerous resources from which you’ll be able to look up syntax if you need it. Over time, you’ll remember common syntax from use and practice.
First, have a 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 utilize them?
It’s best to learn by interacting with the information, not only passively reading books or watching limitless streams of YouTube videos. DataCamp offers many interactive courses, including one on SQL fundamentals. Additionally they offer a SQL Associate Certification.
Learn SQL in context, not syntax
When you’re comfortable retrieving data fields using SELECT, and filtering the information with WHERE clauses, you’re able to experiment with a toy project.
One useful and manageable project is to investigate your personal funds. First, get the information. You would possibly have already got your funds in a spreadsheet. If not, use a spreadsheet to create the information. Make a small (twenty or thirty rows) dataset of your funds. Columns might include the quantity spent, the cardboard or checking account you used, the form of purchase (e.g., utilities, bills, entertainment, transportation, food), date.
Save this data (or your individual) as a .csv file. Now, import this .csv into your DBMS as a table called `credit_card`. The main points vary from one DBMS to a different, but each has a mechanism to import .csv files into tables. For example, for those who’re working in SQL Server, the instructions are here.
When you’ve saved the information, use SQL to question the information using SELECT and WHERE clauses. How much have you ever spent after January? You could possibly write a question just like the next.
SELECT date, amount
FROM credit_card
WHERE date > 'Jan-25'Now, create latest queries. How much do you spend on food per thirty days on average? How much for dining out? In a dataset this small, you could possibly figure this out without using a database, but the purpose here is to learn easy methods to use the technology. After you’ve asked these questions, you’ll be able to expand the dataset and write more queries. What other questions could you answer? Spend every week or two on this project.
Learning Paths
The breadth and depth of SQL you initially must deal with is dependent upon your interests and skilled goals. This video suggests three sample paths (beginner, intermediate, advanced). Let’s have a look at these paths after which map them to common data roles.
The beginner path consists of learning these SQL skills:
- Find out how to use the SELECT statement to retrieve fields from a database
- Find out how to use a WHERE clause to filter data
- Find out how to sort data using the ORDER BY clause
- Find out how to mix tables using JOIN statements
- Find out how to aggregate data using SUM, COUNT, AVG, GROUP BY, and the like
- Find out how to create nested queries (“subqueries”)
The intermediate path adds these skills to the start path.
- Find out how to use Common Table Expressions (CTE) to create temporary results utilized by a bigger query
- Find out how to optimize queries using indexing strategies
- Find out how to automate database tasks with stored procedures and triggers
- Find out how to design higher databases via techniques equivalent to normalization, denormalization, and schema design
- Find out how to use window functions for advanced aggregation
To be an advanced SQL user, also learn the next skills.
- Find out how to prevent SQL injection attacks
- Find out how to use advanced joins in complex queries
- Find out how to optimize queries for large data
- Find out how to pivot data from a row-based format (commonly utilized by relational databases) to a column-based format (commonly utilized by “NoSQL” databases equivalent to MongoDB)
Which path is acceptable based in your skilled interests?
- Beginner: If you might have no experience with SQL, start at the start. This path is acceptable for novices wanting to dip their toe into the information world, or for people who find themselves interested by data but don’t must delve too deeply into the mechanics, equivalent to project managers who wish to run easy queries for themselves.
- Intermediate: For those who already use some SQL but must advance your knowledge, perhaps for a promotion or a profession change, this may be an appropriate path. Junior to mid-level data analysts and data scientists need to know data at this level. You’ll probably wish to take not less than two or three weeks per topic.
- Advanced: Those that need a deep knowledge of knowledge infrastructure, equivalent to data engineers, need to know SQL at a deep level, so that is an appropriate track for them.
Closing Thoughts
Although SQL is usually a large, sprawling language with many corners and nuances, at its heart, it’s a relatively easy to learn computer language, based on easy English statements.
Good luck in your SQL learning journey! One technique to start learning SQL is thru DataCamp’s SQL Fundamentals course. It’s an interesting, powerful language to know.
