To deal with SQL interviews for technology positions involving databases, you need to master five core topics. 1. Filtering and aggregating data: Understand the difference between WHERE and HAVING, master aggregation functions such as SUM and COUNT and date functions, and be able to filter and summarize data according to conditions. 2. Table connection: Be familiar with INNER JOIN, LEFT JOIN and other connection types, and correctly use alias to handle duplicate column names. 3. Subquery and CTE: Use subquery or CTE to split complex logic to improve code readability. 4. Window function: Proficient in ranking and calculation using functions such as ROW_NUMBER, RANK, LEAD/LAG. 5. Practice and expression: Handwritten query sentences and clearly express ideas to demonstrate your understanding and solving problems.

If you're preparing for a tech job that involves databases—like data analyze, backend developer, or data engineer—you'll likely face SQL interview questions. These questions aren't just about syntax; they're designed to test your ability to think through data problems, structure queries, and understand database design. Here's a breakdown of common SQL interview topics and how to approach them.

Filtering and Aggregating Data

Most SQL interviews start with basic querying: filtering rows and summarizing data. You might be asked to calculate total sales per region, find the most recent order for each customer, or count how many users signed up each month.

Key concepts to know:

• WHERE vs HAVING : Use WHERE to filter rows before aggregation, and HAVING after grouping with GROUP BY .
• Common aggregate functions: SUM , COUNT , AVG , MIN , MAX .
• Date functions: DATE_TRUNC , EXTRACT , BETWEEN .

Example:
Say you're asked to find how many orders each customer made in 2023. Your query would involve filtering the orders table by year and grouping by customer ID.

 SELECT customer_id, COUNT(*) AS total_orders
FROM orders
WHERE order_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY customer_id;

This is a common pattern, so practice variations—like filtering with multiple conditions or using HAVING to exclude customers with fewer than X orders.

Joining Tables

Joining tables is a core SQL skill. Interviewers often give you two or more tables and ask you to combine them to answer a business question—like showing all customers along with their latest order, or finding users who haven't placed any orders.

Types of joins to understand:

• INNER JOIN : Returns only matching rows.
• LEFT JOIN : Returns all rows from the left table and matching rows from the right.
• RIGHT JOIN : Similar to LEFT, but starts from the right table.
• FULL OUTER JOIN : Combines all rows from both tables.
• SELF JOIN : Joining a table to itself.

A common mistake is forgetting to handle duplicates or not using table aliases when joining the same table more than once.

Tip: When joining, always specify the join condition clearly and avoid ambiguous column names by using table prefixes or aliases.

Subqueries and Common Table Expressions (CTEs)

Once you're comfortable with joins and aggregations, the next level involves subqueries and CTEs. These are used to break down complex logic into manageable parts.

When to use them:

• When you need to filter based on aggregated results.
• To avoid repeating the same logic in multiple places.
• For ranking or window functions (like finding top N results per group).

Example:
Suppose you want to find customers whose total spending is above average.

 SELECT customer_id, SUM(order_total) AS total_spent
FROM orders
GROUP BY customer_id
HAVING SUM(order_total) > (
    SELECT AVG(total_spent)
    FROM (
        SELECT SUM(order_total) AS total_spent
        FROM orders
        GROUP BY customer_id
    ) AS subquery
);

You can also rewrite this with a CTE for better reading:

 WITH customer_spending AS (
    SELECT customer_id, SUM(order_total) AS total_spent
    FROM orders
    GROUP BY customer_id
)
SELECT *
FROM customer_spending
WHERE total_spent > (SELECT AVG(total_spent) FROM customer_spending);

CTEs are especially useful in interviews because they show you can structure code logically.

Window Functions

Window functions are a bit more advanced but are increasingly common in SQL interviews. They allow you to perform calculations across a set of rows related to the current row—like ranking, running totals, or percentiles.

Common window functions:

• ROW_NUMBER() , RANK() , DENSE_RANK() : For ranking rows.
• LEAD() / LAG() : To access values from the next or previous row.
• SUM() OVER() , AVG() OVER() : Running totals or averages.
• NTILE() : For bucketing data (eg, quartiles).

Example:
To find the top 3 highest-spending customers each month:

 WITH monthly_spending AS (
    SELECT 
        customer_id,
        DATE_TRUNC('month', order_date) AS order_month,
        SUM(order_total) AS total_spent
    FROM orders
    GROUP BY customer_id, order_month
),
ranked_spending AS (
    SELECT *,
           RANK() OVER (PARTITION BY order_month ORDER BY total_spent DESC) AS spending_rank
    FROM monthly_spending
)
SELECT *
FROM ranked_spending
WHERE spending_rank <= 3;

This kind of query tests your ability to layer logic and use advanced SQL features effectively.

SQL interviews vary in difficulty, but most are based on these core areas. Practice writing queries by hand (not relying on autocomplete), and get comfortable explaining your logic step by step. That's usually what interviewers are looking for—not just a correct query, but a clear thought process behind it.

Basically that's it.

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