Advanced SQL Queries: Techniques and Examples

Learn advanced techniques for creating SQL queries. Practical examples and applications.

SQL (Structured Query Language) is a powerful tool that allows for advanced manipulation and analysis of data in databases. As a data analyst, I use SQL daily to extract, transform, and analyze data. In this article, I will present various techniques and examples of advanced SQL queries that will help you better understand how to work efficiently with data.

Introduction

Basic SQL commands like SELECT, INSERT, UPDATE, and DELETE are essential, but advanced techniques allow for more complex data operations. I will discuss concepts such as subqueries, joins, aggregations, window functions, CTEs (Common Table Expressions), and dynamic queries. Each technique will be presented with concrete examples that can be applied in daily work.

Subqueries

Description

Subqueries are queries nested within other queries. They allow for more complex operations, such as filtering data based on the results of other queries.

Example

Let’s say we have an employees table and we want to find employees who earn more than the average salary in the company.

SELECT name, salary

FROM employees

WHERE salary > (SELECT AVG(salary) FROM employees);

Joins

Description

Joins allow for combining data from two or more tables based on related columns. The most commonly used joins are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

Example

We have two tables: employees and departments. We want to display employee names along with their department names.

SELECT employees.name, departments.department_name

FROM employees

INNER JOIN departments ON employees.department_id = departments.id;

Aggregations

Description

Aggregate functions, such as SUM, AVG, COUNT, MAX, and MIN, allow for performing calculations on data sets.

Example

We want to calculate the total sales for each department.

SELECT department_id, SUM(sales) AS total_sales

FROM employees

GROUP BY department_id;

Window Functions

Description

Window functions allow for performing operations on sets of rows that are related to the current row.

Example

We want to calculate the average salary in each department but want the result to include all rows, not just the aggregated ones.

SELECT name, department_id, salary,

       AVG(salary) OVER (PARTITION BY department_id) AS avg_department_salary

FROM employees;

Common Table Expressions (CTE)

Description

CTEs are temporary result sets that can be referred to within subsequent queries. They make complex queries more organized and readable.

Example

We want to find the top sellers in each category.

WITH SalesRank AS (

    SELECT name, category_id, sales,

           RANK() OVER (PARTITION BY category_id ORDER BY sales DESC) AS rank

    FROM employees

)

SELECT name, category_id, sales

FROM SalesRank

WHERE rank = 1;

Dynamic Queries

Description

Dynamic queries allow for creating SQL queries on the fly, which is useful in situations where queries need to be tailored based on input variables.

Example

Let’s say we want to dynamically change the column by which we sort the results.

DECLARE @sortColumn NVARCHAR(50) = 'salary';

DECLARE @sql NVARCHAR(MAX);

SET @sql = 'SELECT name, salary, department_id FROM employees ORDER BY ' + @sortColumn;

EXEC sp_executesql @sql;

List of Advanced SQL Query Techniques

1. Subqueries: Filtering data based on the results of other queries.
2. Joins: Combining data from multiple tables.
3. Aggregations: Performing calculations on data sets.
4. Window Functions: Operations on sets of rows related to the current row.
5. CTE (Common Table Expressions): Temporary result sets that make complex queries more organized.
6. Dynamic Queries: Creating SQL queries on the fly.

Conclusion

Advanced SQL techniques are essential for any data analyst who wants to efficiently process and analyze large datasets. Understanding concepts such as subqueries, joins, aggregations, window functions, CTEs, and dynamic queries allows for performing more complex operations and extracting valuable insights from data. I hope this article helped you understand these advanced techniques and encouraged you to apply them in your daily work.

Other interesting articles:

• Search and Reference Features in Excel: How to Use VLOOKUP, HLOOKUP and XLOOKUP Effectively
• Python Basics: An Introduction for Beginners
• SQL basics: How to start your adventure with databases

Prefer to read in Polish? No problem!

The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.

That’s all on this topic. Analyze in peace!

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