7 Advanced SQL Techniques Every Data Scientist Should Know

 

SQL (Structured Query Language) is a powerful tool for managing and manipulating large datasets, making it an essential skill for data scientists. While most data scientists are familiar with basic SQL queries, advanced SQL techniques can help you efficiently analyze and manipulate large and complex datasets. In this article, we will explore ten advanced SQL techniques that every data scientist should know.

1. Window Functions

Window functions are a powerful feature that allows you to perform calculations on a subset of rows within a result set. This subset is defined by a window that is based on some criteria such as a range of rows or partitions. Common window functions include ROW_NUMBER(), RANK(), and DENSE_RANK().

Sure, here are some real-life examples and code snippets for the SQL window functions ROW_NUMBER(), RANK(), and DENSE_RANK().

• ROW_NUMBER(): The function assigns a unique sequential number to each row within a result set, based on the order specified in the OVER clause. This can be useful for pagination, ranking, or identifying duplicates. Here's an example:

Let’s suppose we have a table called "sales" with columns "customer_id", "order_date", and "amount". We want to rank the customers based on the total amount they spent on orders in descending order:

 

This query will give us a result set with three columns: "customer_id", "total_amount", and "rank". The "rank" column will contain a unique sequential number for each row, based on the descending order of the "total_amount" column.

• RANK(): The function assigns a rank to each row within a result set, based on the order specified in the OVER clause. If two or more rows have the same value, they will receive the same rank, and the next rank will be skipped. Here's an example:

Let’s suppose we have the same "sales" table as before, and we want to rank the customers based on the total amount they spent on orders, with ties included:

This query will give us a result set with three columns: "customer_id", "total_amount", and "rank". The "rank" column will contain a rank for each row, based on the descending order of the "total_amount" column. If two or more rows have the same "total_amount", they will receive the same rank, and the next rank will be skipped.

• DENSE_RANK(): The function assigns a rank to each row within a result set, based on the order specified in the OVER clause. If two or more rows have the same value, they will receive the same rank, and the next rank will not

2. Subqueries

Subqueries allow you to nest one query inside another query. They are useful when you need to retrieve data from multiple tables or when you need to filter data based on specific criteria. Common types of subqueries include correlated subqueries and derived tables.

Example: Finding the highest-paid employee in each department:

This query uses a subquery to find the maximum salary for each department in the employees table, and then selects the employees who have that maximum salary in their respective department.

These are just a few examples of how subqueries can be used in SQL to perform complex queries on multiple tables or datasets.

3. Common Table Expressions (CTEs)

CTEs allow you to define a temporary named result set that can be referenced within a query. This can make complex queries more readable and easier to maintain.

Example: Simplifying complex queries

CTEs can also be used to simplify complex queries by breaking them down into smaller, more manageable parts. Here's an example of how you can use a CTE to simplify a query that involves multiple subqueries:

In this example, two CTEs are used to break down a complex query into smaller parts. The first CTE named "top_customers_cte" selects the top 10 customers based on their total sales. The second CTE named "customer_orders_cte" then selects the total number of orders for each of the top 10 customers. Finally, the main query joins the two CTEs to display the results.

4. Joins 

Joins are used to combine data from two or more tables into a single result set. Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, SELF-JOIN and CROSS-JOIN.

Source: dataquest.io

• Inner Join - the inner join returns only the rows where there is a match in both tables based on the specified join condition. This is the most commonly used join type.
  
  Example:
  Suppose we have two tables, "orders" and "customers", and we want to retrieve the orders and the corresponding customer names.

• Left Join (Left Outer Join) - the left join returns all the rows from the left table and the matching rows from the right table based on the specified join condition. If there is no match in the right table, the result will contain null values for the right table columns.
  
  Example:
  Suppose we have two tables, "customers" and "orders", and we want to retrieve all the customers and the corresponding orders (if any).

• Right Join (Right Outer Join) - the right join is similar to the left join, but it returns all the rows from the right table and the matching rows from the left table based on the specified join condition. If there is no match in the left table, the result will contain null values for the left table columns.
  
  Example:
  Suppose we have two tables, "orders" and "customers", and we want to retrieve all the

• Full Outer Join - the full outer join returns all the rows from both tables and the matching rows based on the specified join condition. If there is no match in one of the tables, the result will contain null values for the columns of the non-matching table.
  
  Example:
  Suppose we have two tables, "customers" and "orders", and we want to retrieve all the customers and all the orders.

• Self-join - a self-join is a join in which a table is joined with itself. This is useful when you have a table that contains a hierarchy or a relationship between rows within the same table. In a self-join, you need to use aliases to differentiate between the two instances of the same table. Here's an example:
  
  This query will return the name of each employee and their manager's name by joining the employees table with itself on the manager_id and employee_id columns.

• Cross-join - A cross join, also known as a Cartesian product, returns a result set that is the combination of every row from two or more tables. This means that every row from the first table is combined with every row from the second table, resulting in a result set that is the product of the two tables.
  
  Cross joins are useful when you want to generate all possible combinations of data from two tables. However, they can also produce very large result sets, especially when working with large tables. Therefore, it is important to use cross joins judiciously and to ensure that you are not generating more data than you need.
  
  Here is an example of a cross join between two tables:

One important thing to keep in mind when using joins is that they can significantly impact the performance of your query. Joining large tables or joining on non-indexed columns can slow down your query and cause performance issues. To optimize your queries, it's important to carefully choose which columns to join on and to ensure that those columns are indexed. Additionally, using appropriate join types and writing efficient queries can help improve the overall performance of your database.

5. Unions

The UNION operator allows you to combine the result sets of two or more SELECT statements into a single result set. The columns in the SELECT statements must have the same data types and appear in the same order in all the SELECT statements. Here's an example of how to use the UNION operator in SQL:

Suppose we have two tables, "employees" and "contractors", each with the same columns: "name", "title", and "salary". We want to combine the data from both tables into a single result set.

The result of the query will be:

In this example, we used the UNION operator to combine the data from the "employees" and "contractors" tables into a single result set. The columns in the SELECT statements are the same, and they appear in the same order, so the UNION operator works as expected.

One interesting thing to note about the UNION operator is that it automatically removes duplicates from the result set. If you want to include duplicates, you can use the UNION ALL operator instead of the regular UNION operator.

6. Pivot Tables

Pivot tables allow you to transform data from rows to columns, making it easier to analyze and summarize data. The PIVOT operator in SQL can be used to pivot data based on a specific column.

Here's an example of how to use the PIVOT function in SQL.

Suppose you have a table called "sales_data" that contains the following data:

Product	Region	Year	Sales
A	East	2018	100
A	West	2018	200
B	East	2018	150
B	West	2018	250
A	East	2019	120
A	West	2019	220
B	East	2019	180
B	West	2019	280

You can use the PIVOT function to create a summary table that shows the total sales for each product and region, with columns for each year.

Here's the code to create this summary table:

The output of this query will be:

Product	Region	2018	2019
A	East	100	120
A	West	200	220
B	East	150	180
B	West	250	280

As you can see, the PIVOT function has transformed the original table into a summary table that shows the total sales for each product and region, with columns for each year. This makes it easier to compare sales across different regions and products, and to identify trends over time.

7. Date Functions

Date and time functions allow you to perform calculations and manipulate dates and times in SQL. Here are some of the most common timestamp and date functions in SQL, along with examples and code structures:

• EXTRACT(): Extracts a specific part of a date or timestamp, such as the year, month, or day.
  
  
  
  
• DATE_TRUNC(): Truncates a timestamp or date to a specific level of precision, such as year, month, or day.
  
  
  
• DATEDIFF(): Calculates the difference between two dates or timestamps.
  

These functions can be extremely useful for a variety of tasks in SQL, such as analyzing trends over time, calculating durations, and formatting dates for reporting purposes.

By mastering these ten advanced SQL techniques, data scientists can more efficiently analyze and manipulate large and complex datasets. Whether you are working with data from a single table or combining data from multiple tables, SQL provides a powerful set of tools that can help you extract valuable insights from your data.