Adventures in Machine Learning

Unlocking Data Insights: Advanced SQL Queries for Powerful Analysis

Advanced SQL Queries for Data Analysis

Data analysts face the challenge of not just understanding their datasets but also of mining insights from them. SQL (Structured Query Language) is a powerful tool that enables analysts to interrogate relational databases, extract relevant information, and gain new insights.

While most analysts are familiar with the basics of SQL, going beyond that beginner/intermediate level requires knowledge of advanced SQL queries. This article discusses some of the top advanced SQL queries for data analysis.

These include grouping data by time period, ranking data using window functions, computing running totals, computing moving averages, and many more. With these advanced SQL queries in hand, data analysts can unlock greater insights from their datasets, improve their analysis capabilities, and ultimately deliver more valuable insights.

1. Grouping Data by Time Period

One of the most common tasks in data analysis is grouping data by time periods.

The SQL EXTRACT function is used to pull out specific values from date or time columns. For example, if you want to group sales data according to the year and month it was generated, you can use the YEAR and MONTH functions as shown in the example below:

SELECT YEAR(sales_date) as Sales_Year, MONTH(sales_date) as Sales_Month, SUM(sales_amount) as Total_Sales FROM sales_data GROUP BY YEAR(sales_date), MONTH(sales_date);

This query groups the sales data by year and month, and sums the sales amount for each group.

2. Creating Multiple Grouping Levels Using ROLLUP

The GROUP BY clause is used to group rows that have the same values into summary rows, like the query in the previous section.

The ROLLUP function is a powerful extension of GROUP BY that enables analysts to create multiple grouping levels with a single query. For example, if you want to group sales data by region, country, and product, you can use the ROLLUP function as shown below:

SELECT region, country, product, SUM(sales_amount) as Total_Sales FROM sales_data GROUP BY ROLLUP(region, country, product) ORDER BY region, country, product;

This query groups the sales data by region, country, and product, and computes the total sales for each group.

The ORDER BY clause sorts the result by region, country, and product. 3.

Ranking Data Using Window Functions

Window functions enable analysts to perform calculations on a set of rows that are related to the current row. The RANK and DENSE_RANK functions are commonly used to rank data based on a specific column.

For example, if you want to rank sales data based on the sales amount, you can use the RANK function as shown below:

WITH ranked_sales_data AS ( SELECT sales_date, sales_amount, RANK() OVER (ORDER BY sales_amount DESC) as Sales_Rank FROM sales_data ) SELECT * FROM ranked_sales_data WHERE Sales_Rank <= 10 ORDER BY Sales_Rank;

This query ranks the sales data by sales amount, computes the rank of each row, and selects the top 10 rows based on the rank. 4.

Computing the Difference (Delta) Between Rows

The LAG function is used to access data from a previous row, allowing analysts to compute the difference (delta) between consecutive rows. For example, if you want to compute the difference in sales amount between consecutive months, you can use the LAG function as shown below:

SELECT sales_date, sales_amount, sales_amount – LAG(sales_amount) OVER (ORDER BY sales_date) as Delta_Sales FROM sales_data ORDER BY sales_date;

This query computes the difference in sales amount between consecutive months, using the LAG function to access the sales amount from the previous row.

5. Computing Running Total

The SUM function can be used in combination with the OVER and ROWS BETWEEN clauses to compute running totals.

For example, if you want to compute the running total of sales amount for each month, you can use the query below:

SELECT sales_date, sales_amount, SUM(sales_amount) OVER (ORDER BY sales_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as Running_Total FROM sales_data ORDER BY sales_date;

This query computes the running total of sales amount for each month, using the SUM function and the ROWS BETWEEN clause to sum all previous rows up to the current row. 6.

Computing Moving Average

The AVG function can be used in combination with the ROWS BETWEEN clause to compute moving averages. For example, if you want to compute the 3-month moving average of sales amount for each month, you can use the query below:

SELECT sales_date, sales_amount, AVG(sales_amount) OVER (ORDER BY sales_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as Moving_Average FROM sales_data ORDER BY sales_date;

This query computes the 3-month moving average of sales amount for each month, using the AVG function and the ROWS BETWEEN clause to average the sales amounts of the current month and the two preceding months.

7. Counting Elements in Custom Categories Using SUM() and CASE WHEN

The SUM function can be used in combination with the CASE WHEN clause to count elements that belong to customized categories.

For example, if you want to count the number of products sold in different categories, you can use the CASE WHEN clause as shown below:

SELECT CASE WHEN product_type = ‘A’ THEN ‘Category 1’ WHEN product_type = ‘B’ THEN ‘Category 2’ ELSE ‘Other’ END as Product_Category, SUM(quantity_sold) as Total_Quantity_Sold FROM sales_data GROUP BY CASE WHEN product_type = ‘A’ THEN ‘Category 1’ WHEN product_type = ‘B’ THEN ‘Category 2’ ELSE ‘Other’ END;

This query counts the total quantity sold for each product category, using the CASE WHEN clause to group products into a customized category.

Conclusion

In conclusion, SQL is a powerful tool for data analysts, and learning advanced SQL queries is essential for gaining a deeper understanding of datasets. With the queries discussed in this article, data analysts can mine insights from their datasets, and deliver valuable and actionable results.

Whether you are working with large datasets or small datasets, there is always room for enhancing your SQL skills and increasing your data analysis capabilities. 3.

Grouping Data by Time Period

3.1 Scenario and Dataset Description

Imagine you are a data analyst at a retail company that sells a wide range of products both online and in-store. Your dataset contains information about the sales of these products, including the product ID, sale date, and the amount sold.

Your task is to analyze the sales data to gain valuable insights for the company. Example Dataset:

| product_id | sale_date | amount |

|————|———–|——–|

| 1 | 2020-01-01 | 100.00 |

| 2 | 2020-01-02 | 200.00 |

| 3 | 2020-02-01 | 150.00 |

| 1 | 2020-02-02 | 120.00 |

| 2 | 2020-03-01 | 300.00 |

| 3 | 2020-03-02 | 180.00 |

3.2 Querying Data with EXTRACT and SUM Functions

To better understand the sales data, you may want to group the data by the month.

You can use the SQL EXTRACT function to extract the month from the sale date and the SUM function to calculate the total sales amount for each month. Example Query:

SELECT EXTRACT(month FROM sale_date) AS Sale_Month, SUM(amount) AS Total_Sales FROM sales_data GROUP BY EXTRACT(month FROM sale_date) ORDER BY Sale_Month;

This query groups the sales data by month and calculates the total sales amount for each month.

The GROUP BY clause groups the sales data by the month extracted from the sale date, while the SUM function calculates the total sales amount for each month. Finally, the ORDER BY clause sorts the results in ascending order of month.

The result of the query is:

| Sale_Month | Total_Sales |

|————|————-|

| 1 | 300.00 |

| 2 | 270.00 |

| 3 | 480.00 |

This output shows the total sales amount for each month, giving the company valuable information about which months were the most profitable. 4.

Creating Multiple Grouping Levels Using ROLLUP

4.1 Explanation of ROLLUP Function

The ROLLUP function is a powerful extension of the GROUP BY clause in SQL that enables analysts to create multiple grouping levels with a single query. The function produces a result set that includes extra rows to represent super-aggregate grouping.

These rows have NULL values for columns that are not included in the current level of grouping. The ROLLUP function can be used to generate subtotals and grand totals in the same query.

To understand how ROLLUP works, let’s continue with the sales data example. Suppose you want to group the sales data by year and month and also compute a grand total for all years and months.

You can use the ROLLUP function as shown below:

Example Query:

SELECT EXTRACT(year FROM sale_date) AS Sale_Year, EXTRACT(month FROM sale_date) AS Sale_Month, SUM(amount) AS Total_Sales FROM sales_data GROUP BY ROLLUP(EXTRACT(year FROM sale_date), EXTRACT(month FROM sale_date)) ORDER BY Sale_Year, Sale_Month;

This query groups the sales data by both year and month, and also computes a grand total for all years and months. The ROLLUP function is used to generate subtotals and a grand total.

The GROUP BY clause groups the sales data by the year and month extracted from the sale date, while the SUM function calculates the total sales amount for each group. Finally, the ORDER BY clause sorts the results in ascending order of year and month.

The result of this query is:

| Sale_Year | Sale_Month | Total_Sales |

|———–|————|————-|

| 2020 | 1 | 300.00 |

| 2020 | 2 | 270.00 |

| 2020 | 3 | 480.00 |

| 2020 | NULL | 1050.00 |

| NULL | NULL | 1050.00 |

The NULL values in the result show the subtotals and grand total generated by the ROLLUP function. The row with NULL in both Sale_Year and Sale_Month is the grand total row, showing the total sales for all years and months.

4.2 Querying ROLLUP Function with Custom Categories

The ROLLUP function can also be used to group data into custom categories. For example, let’s say you want to group the sales data into the following categories: ‘Category 1’ for sales of product 1, ‘Category 2’ for sales of product 2, and ‘Other’ for all other products.

You can achieve this by using the ROLLUP function in combination with a CTE (common table expression) and the CASE WHEN clause. Example Query:

WITH product_category AS ( SELECT product_id, CASE WHEN product_id = 1 THEN ‘Category 1’ WHEN product_id = 2 THEN ‘Category 2’ ELSE ‘Other’ END AS Product_Category, amount FROM sales_data ) SELECT Product_Category, SUM(amount) AS Total_Sales FROM product_category GROUP BY ROLLUP(Product_Category) WHERE Product_Category IS NOT NULL;

This query groups the sales data into the custom categories ‘Category 1’, ‘Category 2’, and ‘Other’, and computes the total sales for each category.

The ROLLUP function is used to generate subtotals and a grand total. The CTE product_category creates a temporary table that assigns each product to its respective category, using the CASE WHEN clause.

Finally, the WHERE clause filters out the rows with NULL values and returns only the categories that are not NULL. The result of this query is:

| Product_Category | Total_Sales |

|——————|————-|

| Category 1 | 220.00 |

| Category 2 | 500.00 |

| Other | 330.00 |

| NULL | 1050.00 |

This output shows the total sales amount for each custom category, including subtotals and a grand total.

The company can use this information to evaluate the sales of different product categories and make informed decisions about future product development and marketing strategies.

In conclusion, the advanced SQL queries discussed in this article provide data analysts with powerful tools for gaining actionable insights from their datasets.

By using techniques like grouping data by time periods and creating multiple grouping levels using ROLLUP, data analysts can better understand the data they are working with and make more informed decisions. With the knowledge and skillset to use these advanced SQL queries, data analysts can become even more valuable assets to their companies.

5. Ranking Data Using Window Functions

5.1 Explanation of Window Functions

Window functions are used in SQL to perform calculations on a set of rows that are related to the current row.

They differ from aggregate functions (like SUM and AVG) in that they preserve the row-level details while performing calculations. Window functions can be useful when you want to rank data based on a specific column or calculate a running total or moving average, as discussed earlier.

The RANK function is one of the most commonly used window functions in SQL. It assigns a rank to each row in a result set based on the value of a particular column.

For example, if you want to rank the sales data based on the sales amount, you can use the RANK function as shown below:

Example Query:

SELECT sale_date, sales_amount, RANK() OVER (ORDER BY sales_amount DESC) AS Sales_Rank FROM sales_data ORDER BY Sales_Rank;

This query ranks the sales data based on the sales amount, using the RANK function. The ORDER BY clause sorts the result set in descending order of sales amount.

The result of the query is:

| sale_date | sales_amount | Sales_Rank |

|————|————–|————|

| 2020-03-01 | 300.00 | 1 |

| 2020-02-01 | 150.00 | 2 |

| 2020-03-02 | 180.00 | 3 |

| 2020-02-02 | 120.00 | 4 |

| 2020-01-02 | 200.00 | 5 |

| 2020-01-01 | 100.00 | 6 |

This output shows the sales data ranked by sales amount.

5.2 Querying Window Functions with DENSE_RANK

Another window function that can be useful in ranking data is the DENSE_RANK function.

This function assigns a rank to each row in a result set based on the value of a particular column, but unlike the RANK function, it does not leave gaps in the ranking sequence when there are ties. For example, if two sales amounts are tied for first place, the next highest sales amount will be ranked third, not second.

Example Query:

WITH ranked_sales_data AS ( SELECT sale_date, sales_amount, DENSE_RANK() OVER (ORDER BY sales_amount DESC) AS Sales_Dense_Rank FROM sales_data ) SELECT * FROM ranked_sales_data WHERE Sales_Dense_Rank <= 3 ORDER BY Sales_Dense_Rank;

This query ranks the sales data based on sales amount using the DENSE_RANK function and selects the top three rows. The result of the query is:

| sale_date

Popular Posts