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Structured Query Language (SQL) is a standard programming language designed for managing data in relational databases. It’s an essential tool in data analysis, offering a rich variety of commands that enable you to create, manipulate, and query data efficiently. Beyond basic SQL commands like SELECT, INSERT, UPDATE, DELETE, this blog will delve into more advanced techniques that can make your data analysis tasks quicker and agile.
1. Using Joins
Joins are used to combine rows from two or more tables, based on a related column between them. The JOIN statement is often used with SELECT to return a dataset that combines information from multiple tables.
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SELECT Orders.OrderID, Customers.CustomerName FROM Orders INNER JOIN Customers ON Orders.CustomerID = Customers.CustomerID; |
This SQL statement selects the Order ID and Customer Name from the Orders and Customers tables, respectively. The INNER JOIN keyword combines rows from both tables where the Customer ID matches in both.
2. Using CASE
The CASE statement goes through conditions and returns a value when the first condition is met.
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SELECT CustomerName, CASE WHEN Country = 'USA' THEN 'Domestic' ELSE 'International' END AS CustomerLocation FROM Customers; |
The SQL above classifies customers as ‘Domestic’ and ‘International’ based on their country of residence.
3. Using Subqueries
A Subquery or Inner query or Nested query is a query within another SQL query and is embedded within the WHERE clause.
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SELECT CustomerName FROM Customers WHERE CustomerID IN (SELECT CustomerID FROM Orders WHERE Quantity > 5); |
This SQL statement uses a subquery to return the names of customers who have placed orders with a quantity greater than 5.
4. Using UNION and UNION ALL
UNION combines the result of two or more SELECT statements, while UNION ALL does the same but also includes duplicate rows. The only requirement for the selectors is that they have the same number of columns with similar data types.
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SELECT column_name(s) FROM table1 UNION ALL SELECT column_name(s) FROM table2; |
Learning these advanced SQL techniques can deepen your understanding of data manipulation and enhance your effectiveness as a data professional. Remember, practice makes perfect!