Advanced SQL for Data Engineering

Advanced SQL is an indispensable tool for retrieving, analyzing, and manipulating substantial datasets in a structured and efficient manner. It is extensively utilized in data analysis and business intelligence, as well as in various domains such as software development, finance, and marketing.

Mastering advanced SQL can empower you to:

• Efficiently retrieve and analyze large datasets from databases.
• Create intricate reports and visualizations to derive meaningful insights from your data.
• Write optimized queries to enhance the performance of your database.
• Utilize advanced features such as window functions, common table expressions, and recursive queries.
• Understand and fine-tune the performance of your database.
• Explore, analyze, and derive insights from data more effectively.
• Provide data-driven insights and make decisions based on solid evidence.

In today's data-driven landscape, the ability to handle and interpret big data is increasingly vital. Proficiency in advanced SQL can render you a valuable asset to any organization that manages substantial amounts of data.

Below are some examples of advanced SQL queries that illustrate the utilization of complex and powerful SQL features:

Using Subqueries in the SELECT Clause

SELECT 
  customers.name, 
  (SELECT SUM(amount) FROM orders WHERE orders.customer_id = customers.id) AS total_spent
FROM customers
ORDER BY total_spent DESC;

This query employs a subquery in the SELECT clause to compute the total amount spent by each customer, returning a list of customers along with their total spending, ordered in descending order.

Using the WITH Clause for Common Table Expressions (CTEs)

WITH 

  top_customers AS (SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 10),

  customer_info AS (SELECT id, name, email FROM customers)

SELECT 

  customer_info.name, 

  customer_info.email, 

  top_customers.total_spent

FROM 

  top_customers 

  JOIN customer_info ON top_customers.customer_id = customer_info.id;

This query uses the WITH clause to define two CTEs, "top_customers" and "customer_info" which simplifies and modularizes the query. The first CTE identifies the top 10 customers based on their total spending, and the second CTE retrieves customer information. The final result is obtained by joining these two CTEs.

Using Window Functions To Calculate Running Totals

SELECT 

  name, 

  amount, 

  SUM(amount) OVER (PARTITION BY name ORDER BY date) AS running_total

FROM 

  transactions

ORDER BY 

  name, date;

This query utilizes a window function,`SUM(amount) OVER (PARTITION BY name ORDER BY date)`, to calculate the running total of transactions for each name. It returns all transactions along with the running total for each name, ordered by name and date.

Using Self-Join

SELECT 

  e1.name AS employee, 

  e2.name AS manager

FROM 

  employees e1 

  JOIN employees e2 ON e1.manager_id = e2.id;

This query employs a self-join to link a table to itself, illustrating the relationship between employees and their managers. It returns a list of all employees and their corresponding managers.

Using JOIN, GROUP BY, HAVING

SELECT 

  orders.product_id, 

  SUM(order_items.quantity) AS product_sold, 

  products.name

FROM 

  orders 

  JOIN order_items ON orders.id = order_items.order_id

  JOIN products ON products.id = order_items.product_id

GROUP BY 

  orders.product_id

HAVING 

  SUM(order_items.quantity) > 100;

This query uses JOIN to combine the orders and order_items tables on the order_id column, and joins with the product table on the product_id column. It then uses the GROUP BY clause to group results by product_id and the HAVING clause to filter products with more than 100 units sold. The SELECT clause lists the product_id, total quantity sold, and product name.

Using COUNT() and GROUP BY

SELECT 

  department, 

  COUNT(employee_id) AS total_employees

FROM 

  employees

GROUP BY 

  department

ORDER BY 

  total_employees DESC;

This query uses the COUNT() function to tally the number of employees in each department and the GROUP BY clause to group results by department. The SELECT clause lists the department name and total number of employees, ordered by total employees in descending order.

Using UNION and ORDER BY

(SELECT id, name, 'customer' AS type FROM customers)

UNION

(SELECT id, name, 'employee' AS type FROM employees)

ORDER BY name;

This query uses the UNION operator to combine the results of two separate SELECT statements—one for customers and one for employees — and orders the final result set by name. The UNION operator removes duplicates if present.

Recursive Queries

A recursive query employs a self-referencing mechanism to perform tasks, such as traversing a hierarchical data structure like a tree or graph.

Example:

WITH RECURSIVE ancestors (id, parent_id, name) AS (

    -- Anchor query to select the starting node

    SELECT id, parent_id, name FROM nodes WHERE id = 5

    UNION

    -- Recursive query to select the parent of each node

    SELECT nodes.id, nodes.parent_id, nodes.name FROM nodes

    JOIN ancestors ON nodes.id = ancestors.parent_id

)

SELECT * FROM ancestors;

This query uses a CTE called "ancestors" to define the recursive query with columns: id, parent_id, and name. The anchor query selects the starting node (id = 5), and the recursive query selects each node's parent, joining it with the "ancestors" CTE on the parent_id column. This process continues until the root of the tree is reached or the maximum recursion level is attained. The final query retrieves all identified ancestors.

While recursive queries are potent, they can be resource-intensive; therefore, they should be used judiciously to avoid performance issues. Ensure proper recursion termination and consider the maximum recursion level permitted by your DBMS.

Not all SQL implementations support recursion, but major RDBMS systems such as PostgreSQL, Oracle, SQL Server, and SQLite do support recursive queries using the WITH RECURSIVE keyword.

These examples showcase just a few of SQL's powerful capabilities and the diverse types of queries you can construct. The specific details of the queries will depend on your database structure and the information you seek to retrieve, but these examples should provide a foundational understanding of what is achievable with advanced SQL.