Advanced SQL Techniques for Data Analysis

In the field of Data Analysis, Structured Query Language (SQL) is an indispensable skill. The versatility and broad adoption of SQL make it a crucial tool for data analysts. In this post, we delve deeper into more advanced SQL techniques that can provide more insights from your data.

Complex Joins

In SQL, JOIN operations are used to combine rows from two or more tables, based on a related column between them. Besides the basic INNER JOIN and LEFT JOIN, SQL offers other types of join operations such as RIGHT JOIN, FULL JOIN, CROSS JOIN, and SELF JOIN:

SELECT Orders.OrderID, Customers.CustomerName, Orders.OrderDate
FROM Orders
FULL JOIN Customers
ON Orders.CustomerID = Customers.CustomerID
ORDER BY Orders.OrderDate;

The above SQL statement selects all records from Orders and Customers, and combines them where there’s a match on CustomerID.

Window Functions

Window functions perform calculations across a set of table rows that are somehow related to the current row, often used in analytics computations. SQL window functions include RANK(), DENSE_RANK(), ROW_NUMBER(), LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE(), and NTH_VALUE().

SELECT name, salary, 
RANK() OVER (ORDER BY salary DESC) salary_rank
FROM Employees;

The above SQL statement ranks employees based on their salary.

Stored Procedures

Stored procedures are prepared SQL codes that you can save and reuse. This way, the SQL engine can execute the queries much faster since the queries have been parsed and optimized. You can also pass parameters to a stored procedure, so that the stored procedure can act based on the parameter value(s).

CREATE PROCEDURE SelectAllCustomers @City nvarchar(30), @Country nvarchar(30)
AS
SELECT * FROM Customers
WHERE City = @City AND Country = @Country
GO;

The above SQL statement creates a stored procedure that selects all fields from Customers where City and Country match the input parameters.

These are just a few examples of the advanced SQL techniques data analysts can use to get more insights from their data. Stay tuned for our next posts where we’ll dive deeper into each of these topics!