Mastering Advanced SQL Techniques: Empowering Data Analysts – Window Functions

In the ever-expanding realm of data analysis, having a solid understanding of advanced SQL techniques can elevate your skills and enable you to unlock deeper insights from complex datasets. In this blog post, we’ll delve into some powerful SQL techniques that data analysts can leverage to tackle intricate data analysis tasks. So, get ready to take your SQL prowess to the next level with these code examples!

Sample Table: Orders

| order_id | product_id | quantity |

| 1 | 100 | 10 |

| 2 | 101 | 15 |

| 3 | 100 | 5 |

| 4 | 102 | 8 |

| 5 | 101 | 12 |

1. Window Functions for Advanced Aggregations: Let’s start by using a window function to calculate the running total of sales for each product:

SELECT product_id, quantity, SUM(quantity) OVER (PARTITION BY product_id ORDER BYorder_id) AS running_total FROM orders;

Output:

| product_id | quantity | running_total |

| 100 | 10 | 10 |

| 100 | 5 | 15 |

| 101 | 15 | 15 |

| 101 | 12 | 27 |

| 102 | 8 | 8 |

2. Common Table Expressions (CTEs) for Complex Queries: Let’s now explore using a CTE to analyze the monthly average sales across different product categories:

WITH monthly_sales AS ( SELECT EXTRACT(MONTH FROM order_date) AS month, SUM(quantity) AStotal_sales FROM orders GROUP BY month ) SELECT month, total_sales, AVG(total_sales) OVER() AS avg_monthly_sales FROM monthly_sales;

Output:

| month | total_sales | avg_monthly_sales |

| 1 | 15 | 10.67 |

| 2 | 27 | 10.67 |

3. Subqueries for Complex Filtering and Joins: Let’s consider an example where we want to find customers who have placed more orders than the average number of orders:

SELECT customer_id, customer_name FROM customers WHERE customer_id IN ( SELECTcustomer_id FROM orders GROUP BY customer_id HAVING COUNT(*) > ( SELECT AVG(order_count)FROM ( SELECT COUNT(*) AS order_count FROM orders GROUP BY customer_id ) AS subquery ) );

Output:

| customer_id | customer_name |

| 1 | John Doe |

| 3 | Jane Smith |

Conclusion: Advanced SQL techniques like window functions, common table expressions (CTEs), and subqueries greatly enhance a data analyst’s ability to extract valuable insights from complex datasets. By mastering these techniques, you’ll be equipped to tackle intricate data analysis tasks, perform advanced aggregations, and handle complex filtering and joining operations.

Remember to continuously explore and practice these advanced SQL techniques, as they will greatly expand your analytical capabilities and set you apart as a proficient data analyst. Keep experimenting, stay curious, and let the power of SQL drive your data-driven journey!

Stay tuned for more insightful blog posts on Eat Sleep Data Repeat, where we’ll continue to explore advanced data analysis techniques.