Funciones agregadoras en MySQL para análisis eficiente de datos

Mastering MySQL aggregator functions such as COUNT, SUM, AVG and using advanced statements such as CASE and GROUP BY is key to perform effective business analysis from basic information in a database, especially in business applications.

What are aggregator functions and when to use them?

Aggregator functions process groups of records and return a single meaningful value that summarizes the data. The most common ones in MySQL include:

• COUNT: counts records or rows.
• SUM: sums numeric values of a specific column.
• AVG: calculates the average of numeric values.

Practical uses are essential to understand relevant business metrics such as average prices, total amount in inventory, and total money spent on inventory.

How to use COUNT in basic queries?

To count items in a table, we use:

SELECT COUNT(*) FROM PRODUCTS;

You can refine queries with specific conditions using the WHERE clause:

SELECT COUNT(*) FROM PRODUCTS WHERE PRICE < 500;

How useful are SUM and AVG in business analysis?

• Total inventory:

SELECT SUM(STOCK) FROM PRODUCTS;

• Average prices:

SELECT AVG(PRICE) FROM PRODUCTS;

• Money spent on stock (calculate by multiplying price by stock):

SELECT SUM(PRICE  * STOCK) FROM PRODUCTS;

How to categorize data with the CASE and GROUP BY function?

Categorizing information allows us to segment and analyze large databases efficiently. Use the CASE function to create conditional columns:

SELECT  EMAIL,  CASE    WHEN  EMAIL LIKE '%@gmail.com' THEN 'Gmail'    WHEN  EMAIL LIKE '%@hotmail.com' THEN 'Hotmail'    WHEN  EMAIL LIKE '%@yahoo.com' THEN 'Yahoo'    ELSE 'Other provider'  END AS PROVIDERFROM CLIENTS;

Once the information has been categorized, GROUP BY allows grouping records according to the category created:

SELECT  CASE    WHEN  EMAIL LIKE '%@gmail.com' THEN 'Gmail'    WHEN  EMAIL LIKE '%@hotmail.com' THEN 'Hotmail'    WHEN  EMAIL LIKE '%@yahoo.com' THEN 'Yahoo'    ELSE 'Other supplier'  END AS SUPPLIER,  COUNT(*) AS TOTAL_CLIENTSFROM CLIENTSGROUP BY SUPPLIER;

What does the HAVING clause do in our groupings?

HAVING filters the results of a grouping according to a specific condition. Example:

...GROUP BY SUPPLIERHAVING TOTAL_CUSTOMERS < 100;

This returns only those supplier groups with less than 100 customers.

What are best practices for writing efficient queries?

• Keep queries simple, specific and clear.
• Avoid bringing unnecessary noise; process relevant data in each query.
• Always group(GROUP BY) on categorical columns.
• Use clear and efficient WHERE and HAVING conditions to reduce the amount of data processed.

Apply these concepts to obtain valuable business insights and optimize the performance of your databases. Are you already practicing these functions in your own databases? Let us know about your experiences.