Understand Tables, Rows and Columns

Before writing queries, you should understand how information is organized inside a database.

• A table represents a business subject, such as customers or orders.
• A row represents one individual record.
• A column represents one type of information.

For example, a customer table may contain customer ID, name, email, city and registration date. An orders table may contain order ID, customer ID, order date, quantity and sales amount.

Learn Basic SELECT Queries

The SELECT statement retrieves information from a table.

SELECT customer_name, city
FROM customers;

At this stage, practise:

• Selecting all or specific columns
• Renaming columns with aliases
• Removing duplicates with DISTINCT
• Limiting the number of returned records

Filter Data with WHERE

Businesses rarely need every record in a database. Analysts normally work with a particular customer group, category, location or date range.

SELECT *
FROM orders
WHERE sales_amount > 500;

You should learn how to use:

• Comparison operators
• AND and OR
• IN for multiple values
• BETWEEN for ranges
• LIKE for text matching
• IS NULL for missing values

Sort and Prioritize Results

The ORDER BY clause helps analysts identify top-performing, lowest-performing or most recent records.

SELECT product_name, sales_amount
FROM products
ORDER BY sales_amount DESC;

Sorting can help identify highest-revenue products, lowest-performing regions, recent transactions or customers with the highest spending.

Calculate Summary Statistics

Analysts often need summarized results instead of individual records.

• COUNT() counts records.
• SUM() calculates totals.
• AVG() calculates averages.
• MIN() identifies the smallest value.
• MAX() identifies the largest value.

SELECT
    COUNT(*) AS total_orders,
    SUM(sales_amount) AS total_revenue,
    AVG(sales_amount) AS average_order_value
FROM orders;

Group Data with GROUP BY

The GROUP BY clause calculates separate results for different categories.

SELECT region, SUM(sales_amount) AS total_revenue
FROM orders
GROUP BY region;

You can group data by:

• Product category
• Customer segment
• City or region
• Month or year
• Payment method
• Marketing channel

Filter Summarized Results with HAVING

The WHERE clause filters individual records before grouping. The HAVING clause filters summarized results after grouping.

SELECT customer_id, SUM(sales_amount) AS total_spent
FROM orders
GROUP BY customer_id
HAVING SUM(sales_amount) > 5000;

This query identifies customers whose total spending is greater than $5,000.

Combine Tables with JOINs

Business information is usually divided among customers, orders, products and other related tables. SQL joins combine this information.

SELECT
    customers.customer_name,
    orders.order_date,
    orders.sales_amount
FROM customers
INNER JOIN orders
    ON customers.customer_id = orders.customer_id;

Data analysts should understand:

• INNER JOIN
• LEFT JOIN
• Primary and foreign keys
• One-to-one and one-to-many relationships
• Duplicate rows caused by incorrect joins

Create Business Categories with CASE WHEN

The CASE WHEN statement creates meaningful categories based on business rules.

SELECT
    customer_name,
    total_spent,
    CASE
        WHEN total_spent >= 5000 THEN 'High-Value Customer'
        WHEN total_spent >= 2000 THEN 'Medium-Value Customer'
        ELSE 'Standard Customer'
    END AS customer_segment
FROM customers;

This technique can classify customer value, product performance, order size, inventory status or business risk.

Learn Subqueries and CTEs

Once you understand filtering, grouping and joins, begin working with subqueries and Common Table Expressions.

WITH customer_sales AS (
    SELECT
        customer_id,
        SUM(sales_amount) AS total_spent
    FROM orders
    GROUP BY customer_id
)

SELECT *
FROM customer_sales
WHERE total_spent > 5000;

CTEs help divide a complex analysis into smaller and more readable steps.

Validate Your Results

A SQL query can run without errors and still produce an incorrect business answer. Professional analysts validate their results before presenting them.

• Did a join create duplicate records?
• Are missing values affecting the calculation?
• Is the selected date range correct?
• Are cancelled or returned orders included?
• Does the number of records make sense?
• Are customer and product IDs unique?
• Do the totals match a trusted report?