These 20 basic queries are a must in a starter pack for every SQL beginner. These examples will get you going on your journey to mastering SQL.

You’ve set your mind on learning SQL, googled ‘basic sql query examples’ or something similar, and here you are staring at this article. Now what? All learning starts with the basics, so let’s start with the most basic question:

SQL, or Structured Query Language, is a programming language. Like any language – programming or natural – it is used to communicate, to talk. SQL is designed to talk to a database. We do that using sentences that we call queries, which are SQL commands for retrieving data from the database.

We’ll soon show you 20 basic SQL query examples to start talking with the database. All these queries are taught in our SQL Basics course. This course will give you even more structure, examples, and challenges to solve. It has 129 interactive exercises on querying one or more tables, aggregating and grouping data, JOINs, subqueries, and set operations. Even with the 20 upcoming examples, we won’t show all the details or even all the basic-level queries. That’s why we recommend using the course as a platform for practicing the fundamentals we’ll discuss here.

Also, most of our examples are nicely presented in our SQL Basics Cheat Sheet. Feel free to have it by your side – it might help you better understand what follows next.

Our dataset consists of two tables. The first table is called employees.

The columns and data in the above table are:

All this tells us that this table is a list of a company’s employees and their salaries. There is also data on the employees’ departments. All employees work in the sales division, where the department can be either Corporate or Private Individuals. In other words, the employees sell the company’s products to companies and private individuals.

The other table in the dataset is named quarterly_sales.

The columns are:

In general, this table is a list of each quarter’s sales made by every employee shown in the first table.

Now, let’s start writing SQL queries.

This query is useful when you want to quickly get all the columns from a table without writing every column in the SELECT statement.

Whenever you want to select any number of columns from any table, you need to use the SELECT statement. You write it, rather obviously, by using the SELECT keyword.

After the keyword comes an asterisk (*), which is shorthand for “all the columns in the table”.

To specify the table, use the FROM clause and write the table’s name afterward.

The query’s output is the whole table employees, as shown below.

You can use this query when you only need one column from the table..

The approach is similar to the previous query. However, this time, instead of an asterisk, we write the specific column name in SELECT. In this case, it’s the column first_name.

The second line of the query is the same: it references the table in the FROM clause.

The query returns the list of employees’ first names.

This query is useful when selecting two (or more) columns from one table.

Again, the approach is similar to earlier examples. To select two columns, you need to write their names in SELECT. The important thing is that the columns need to be separated by a comma. You can see in the example that there’s a comma between the columns first_name and last_name.

Then, as usual, reference the table employees in FROM.

Now the query shows the employees’ full names.

Knowing this SQL query will allow you to filter data according to numeric values. You can do that using comparison operators in the WHERE clause.

Here’s the overview of the SQL comparison operators.

The query actually selects three, not two columns.  It’s the same as with two columns: simply write them in SELECT and separate them with commas.

Then we reference the table in FROM.

Now, we need to show only employees with a salary above 3,800. To do this, you need to use WHERE. It’s a clause that accepts conditions and is used for filtering the output. It goes through the table and returns only the data that satisfies the condition.

In our case, we’re looking for salaries ‘greater than’ a certain number. In other words, a condition using the > comparison operator.

To set the condition, we write the column name in WHERE. Then comes the comparison operator, and after that, the value that the data has to be greater than. This condition will now return all the salaries that are above 3,800.

The query returns four employees and their salaries. As you can see, they all have salaries above 3,800.

Once again, this basic SQL query example is useful when you want to select several columns but not all the rows from the table. Now you want to find the values that are the same as the value from the condition. For that, you need the equality condition (=).

The query selects employees’ first and last names.

However, we want to show only employees whose name is Luca. For this, we again use WHERE. The approach is similar to the previous example: we use WHERE, write the column name, and use the comparison operator. This time, our condition uses the equal sign (=).

In other words, the values in the column first_name have to be equal to Luca. Also, when the condition is not a number but a text or a date/time, it has to be written in single quotes (''). That’s why our condition is written as 'Luca', not simply Luca.

The output shows there’s only one employee named Luca, and his full name is Luca Pavarotti.

Here’s another basic SQL query example that you’ll find useful. It can be used whenever you have to order the output in a certain way to make it more readable.

Ordering or sorting the output is done using the ORDER BY clause. By default, it orders the output in ascending order.  This works alphabetically (for text data), from the lowest to the highest number (for numerical data), or from the oldest to the newest date or time (for dates and times).

We again select employees’ first and last names. But now we want to sort the output in a specific way. In this example, it’s by employees’ surname. To do that, we use ORDER BY. In it, we simply write the column name.

We might add the keyword ASC after that to sort the output ascendingly. However, that’s not mandatory, as ascending sorting is a default in SQL.

The query returns a list of employees ordered alphabetically by their last names.

This example is similar to the previous one and has the same purpose: sorting your SQL query output. However, in this case, the data is ordered in descending order (Z to A, 10 to 1).

The query is almost exactly the same as in the previous example. The only difference is we’re ordering the output by the employee’s name descendingly.

To do that, put the keyword DESC after the last_name column in the ORDER BY clause.

You can see that the output is ordered the way we wanted.

Sorting an SQL query can get more sophisticated. It’s common to sort data by two or more columns, which you’re probably already familiar with as an Excel or Google Sheets user. The same can be done in SQL.

With this query, we’re building on the previous example; we want to sort the output by the employee’s salary and their last name. This time, we sort by salary descending and then by last name ascendingly.

We reference the column salary in ORDER BY and follow it with the keyword DESC. The DESC keyword indicates descending order. Before the second ordering criteria, we need to put a comma. After it comes the second criteria/column, which is last_name in this case. You can add or omit the keyword ASC to sort the output in ascending order.

Note: The order of the columns in ORDER BY is important! The query written as it is above will first sort by salary descendingly and then by the last name ascendingly. If you wrote ORDER BY last_name ASC, salary DESC, it would sort by last name first and then by salary in descending order.

The output is ordered by salary. When the salary is the same (green rows), the data is ordered alphabetically by last name.

This example will again demonstrate how to filter output using WHERE. It will be a bit more advanced this time, as we’ll use a logical operator. In SQL,  logical operators allow you to test if the filtering condition is true or not. They also allow you to set multiple conditions.

The three basic logical operators in SQL are AND, OR, and NOT. In the query below, we’ll  use OR to get salaries below 3,000 or above 5,000.

We use this query to select the employee’s first name, last name, and salary from the table employees.

However, we want to show only those employees whose salaries are either above $5,000 or below $3,000. We do that by using the logical operator OR and the comparison operators in WHERE.

We write the first condition in WHERE, where we reference the salary column and set the condition that the values must be above 5,000. Then we use the OR operator, followed by the second condition. The second condition again references the salary column and uses the ‘less than’ operator to return the values below 3,000.

The query returns only three employees and their salaries, as they are the only ones that satisfy the conditions.

In this example, we’ll show how you can perform simple mathematical operations on the table’s columns.

We’ll use one of SQL’s arithmetic operators.

In the above query, we want to find the sales in the first half of 2022 for each employee.

We do it by first selecting the column employee_id from the table quarterly_sales.

Then we select the column q1_2022 and use the addition arithmetic operator to add the q2_2022 column. We also give this new calculated column an alias of h1_2022 using the AS keyword.

The output shows all the employees’ IDs and their respective sales in the first half of 2022.

This query uses the aggregate function SUM() with GROUP BY. In SQL, aggregate functions work on groups of data; for example, SUM(sales) shows the total of all the values in the sales column.  It’s useful to know about this function when you want to put data into groups and show the total for each group.

The purpose of the above query is to find the total salary amount for each department. This is achieved in the following way.

First, select the column department from the table employees. Then, use the SUM() function. As we want to add the salary values, we specify the column salary in the function. Also, we give this calculated column the alias total_salaries.

Finally, the output is grouped by the column department.

Note: Any non-aggregated column appearing in SELECT must also appear in GROUP BY. But this is logical – the whole purpose is to group data by department, so of course we’ll put it in GROUP BY.

The output shows all the departments and the sum of total monthly salary costs by department.

Here’s another basic SQL query that uses an aggregate function. This time, it’s COUNT(). You can use it if you want to group data and show the number of occurrences in each group.

We want to show the number of employees by department.

Select the department from the table employees. Then, use the COUNT() aggregate function. In this case, we use the COUNT(*) version, which counts all the rows. We give the column the alias employees_by_department.

As a final step, we group the output by the department.

Note: COUNT(*) counts all the rows, including those with the NULL values. If you don’t want to include the possible NULL values in your output, use the COUNT(column_name) version of the function. We can use COUNT(*) here because we know no NULL values are in the table.

There are two departments, each with five employees.

The AVG() function calculates the average value. You can use this query whenever you want to group data and show the average value for each group.

The query is the same as the last one, only this time we use the AVG() function, as we want to calculate the average salary by department.

We select the department, use AVG() with the salary column, and group the output by department.

The output shows two departments and their average salaries.

This is another query that combines an aggregate function with GROUP BY. Use it whenever you want to find the minimum values for each group.

Again, we use the same query and change only the aggregate function.

The query calculates the minimum salary by department.

The output shows the departments and the lowest salary in each department.

This example shows how to use the MAX() aggregate function to show the highest value within each group.

We use the query to show the highest salary in each department, together with the department’s name.

You already know how this works. The query is the same as in the previous example, but now it uses the MAX() function.

The output shows us the highest salaries in the Corporate and Private Individuals department.

This one might seem more complicated, but it’s still a basic SQL query. It is used when you want to show the total values for each group but you want to include only specific rows in the sum.

The query will show the total salary by department, but it will include only individual salaries above $3,500 in the sum. Here’s how it works.

First, of course, select the departments and use SUM() with the salary column from the table employees. You learned that already.

Then use the WHERE clause to specify the values you want included in the sum. In this case, it’s where the column salary is higher than 3,500. In other words, the query will now sum only values above 3,500.

Finally, group by department.

These totals now include only salaries above $3,500. Compare this to the output from the eleventh example (shown below; mind the different sorting), and you鈥檒l see that the totals are lower. It鈥檚 logical, as the below output also includes salaries equal to or less than $3,500.

This is also one of the queries we advise you to include in your SQL toolbox. It’s similar to the previous one, as it uses an aggregate function. This type of query can be used when you want to show the number of occurrences for each group.

This is similar to the previous query, only it uses the COUNT() aggregate function. Its goal is to show the department name and the number of employees in that department, but it counts only the employees with a salary above $3,500.

To achieve that, first select the department. Then use COUNT(*) to count all the rows within each department. Each row equals one employee. We are free to use this version of the COUNT() function because we know there are no NULL rows.

Now, use WHERE to include only employees with salaries higher than $3500 in the counting.

In the end, you only need to group data by department.

The output shows there are three employees in the Private Individuals department paid above $3,500 and there are four such employees in the Corporate department.

Some employees are obviously missing, as they should be. We learned in one of the previous examples that there are five employees in each department.

This type of query is used whenever you want to access data from two or more tables. We’ll show you INNER JOIN, but it’s not the only join type you can use.

Here’s a short overview of join types in SQL. These are the full join names. What’s shown in the brackets can be omitted in the query and the join will work without it.

This query wants to show each employee’s ID and name, together with their total sales in 2022.

For that, it uses JOIN, as the required data is in both tables of our dataset.

Let’s start explaining the query with the FROM clause. This is familiar: to use the data from the table employees, you need to reference it in FROM. We also give this table an alias (‘e’), so that we don’t have to write the table’s full name later on.

After that, we use the JOIN keyword to join the second table. We do that by referencing the table quarterly_sales in JOIN and giving it the alias ‘qs’.

Now comes the ON condition. It is used to specify the columns on which the two tables will be joined. Usually, those are the columns that store the same data in both tables. In other words, we join the tables on the primary and foreign keys. A primary key is a column (or columns) that uniquely defines each row in the table. A foreign key is a column in the second table that refers to the first table. In our example, the column id from the table employees is its primary key. The column employee_id from the table quarterly_sales is the foreign key, as it contains the value of the column id from the first table.

So we’ll use these columns in ON, but we also need to specify which table each column is from. Remember, we gave our tables aliases. This will come in handy here, as we won’t need to write the tables’ full names – only one letter for each table. We write the first table’s alias (instead of its full name), separate them with a dot, and then the column name. We put the equal sign, the second table’s alias, and the column name.

Now that we have two tables joined, we are free to select any column from both tables.  We select id, first_name, and last_name from employees. Then we add each column from the table quarterly_sales showing the quarterly sales and name it total_sales_2022. Each column in SELECT also has the table alias before it, with the alias and the column name separated by a dot.

Note: When joining tables, using the table names in front of the column names in SELECT is advisable. This will make it easier to determine which column comes from which table. Also, the tables can have columns of the same name. However,  table names can become wordy, so giving them aliases in JOIN is also advisable. That way, you can use much shorter aliases (instead of the full table names) in front of the column names.

The output lists each employee and shows their total sales in 2022.

Of course, you can filter data in joined tables the same way as you can with only one table. You’ll again need the WHERE clause.

We tweaked the previous query to show the decrease in sales between the third and the fourth quarter.

Here’s how we did it. Just as we did earlier, we selected the employee’s ID and name.

We subtracted one quarter from another to calculate the change between the quarters. In this case, it’s the column with the fourth quarter sales minus the third quarter sales. This new column is named sales_change.

The tables are joined exactly the same way as in the previous example.

To show only the sales decrease, we use the WHERE clause. In it, we again subtract the third quarter from the fourth and set the condition that the result has to be below zero, i.e. a decrease. As you noticed, WHERE comes after the tables are joined.

The output shows all the employees who had a sales decrease in the last quarter and the amount of that decrease.

You probably noticed that outputs in our two latest examples are sorted a bit randomly. This is not something you have to put up with – you can order data with ORDER BY even when using two tables.

The query is not much different from the previous one. We again select the employee’s ID and name. We also add the sales in the last quarter of the year. The tables are then joined the same way as earlier. We use the WHERE clause to show only quarterly sales above $5,000.

Also, we want to sort the output. This is not different from what we learned earlier: simply write the column name in ORDER BY and sort it the way you want. In our example, we are sorting from the highest to the lowest quarterly sales.

The output shows all five employees whose sales were above $5,000 in the last three months of 2022.

If you want to master SQL, you must be comfortable using these 20 basic SQL queries. These are the fundamentals that will allow you to build solid SQL knowledge.

This kind of knowledge is achieved by a lot of practice and experience. You need to write the queries on your own. Our interactive SQL Basics course is brimming with basic SQL query examples! It contains over 100 interactive SQL exercises for you to solve on your own. Try it, and we’re sure you won’t regret it!