SQL Interview Questions for Data Analysts: 15 Must-Know Questions in 2026

Table of Contents,

SQL Interview Questions: 15 Must-Know Questions for Data Analysts in 2026

Part 1 of 100 SQL + Business Interview Questions Series

Every type of Questions explained with actual business context, example queries, sample output, and interview-ready answers.

Introduction

SQL Interview Questions for Data Analysts are one of the most important topics for interview preparation in 2026. Greetings, I’m Priyanka, a beginner data learner blogging my experience preparing for interviews at Bloomindata.

SQL interview questions are one of the most important topics for aspiring data analysts preparing for interviews in 2026.

SQL seemed like the toughest obstacle to overcome when I first started getting ready for data analyst interviews. “You need to know SQL” was a common reply, but in an interview context, nobody really explained what that meant.

I therefore created this book, which includes 15 of the most commonly asked SQL interview questions along with appropriate responses, real-world business examples, and explanations that are understandable to beginners like us.

This post is for you if you’re getting ready for your first job as a data analyst in 2026 or reviewing material for an upcoming interview. Now let’s get started.

These SQL Interview Questions for Data Analysts help beginners understand real business scenarios.

Q1. What is SQL?

Practicing SQL Interview Questions for Data Analysts regularly improves query-writing skills.

Structured Query Language is what SQL stands for. Data from relational databases is stored, retrieved, altered, and analyzed using it. SQL is primarily used by data analysts to query company data, generate reports, and uncover insights that support decision-making.

Why companies ask this:

This is almost always the first question in an interview. They are checking whether you understand the basic purpose of SQL and whether you can explain it in plain language without over-complicating it.

Real applications of SQL in business:

Examining monthly sales data
Tracking consumer behavior
Creating dashboards for KPIs
Identifying patterns across time

For example, the monthly revenue query

SELECT MONTH(order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY MONTH(order_date);

This query finds the total revenue for each month by grouping all orders by month. This is used by a company to track growth over time.

For example, the Top 5 Spending Customers:

SELECT customer_id,
SUM(amount) AS total_spent
FROM orders
GROUP BY customer_id
ORDER BY total_spent DESC
LIMIT 5;

This helps the marketing team in identifying VIP customers for loyalty programs.

Q2. What is the Difference Between SQL and MySQL?

A language called SQL is used to interact with databases. MySQL is a software program that employs SQL as its programming language and is a relational database management system.

Consider it this way: MySQL is like a browser, and SQL is like English. The language is used by the browser, but the browser is not the language.

Term	What it is
SQL	The language (used to write queries)
MySQL	The database software (stores the data)

Real workflow example:

Step	Tool
Store data	MySQL (or PostgreSQL, SQL Server)
Query data	SQL
Visualize	Power BI or Tableau

Interview tip: In most analyst interviews, when they say, “Do you know SQL?” they mean, “Can you write queries?” They are not asking you to manage a MySQL server. Focus on query writing skills.

Q3. What Does SELECT Do?

The simplest SQL command is SELECT. Data is retrieved from a table using it. Using SELECT *, you can retrieve all columns or just the ones that are necessary for your report.

Example 1: Extract Every Column:

SELECT * FROM customers;

Use this for fast debugging or inspection. Large production tables are not advised.

Example 2: Find Just What You Need

SELECT customer_name, city
FROM customers;

It’s best to do this. It is quicker and requires less memory to fetch only the necessary columns.

Example 3: Use of Business Reporting

SELECT order_id, amount
FROM orders
WHERE amount > 1000;

As finance teams tracking big orders, this identifies all high-value transactions.

Interview tip: When you point out that SELECT * is not a good practice for large tables, interviewers will enjoy it. It demonstrates that you consider performance rather than just syntax.

Most companies ask SQL Interview Questions for Data Analysts during technical interview rounds.

Q4. What is the Difference Between WHERE and HAVING?

WHERE filters rows before aggregation happens. HAVING filters after data has been grouped using GROUP BY.

SQL processes your query in this order:

FROM → WHERE → GROUP BY → HAVING → SELECT

Writing accurate queries requires an understanding of this execution order.

For example, WHERE (filters individual rows):

SELECT *
FROM employees
WHERE salary > 50000;

Before any grouping, rows with a salary of $50,000 or less are eliminated.

For example, HAVING (filters aggregated groups):

SELECT department, AVG(salary)
FROM employees
GROUP BY department
HAVING AVG(salary) > 60000;

The average salary for each department is first determined by grouping all employees by department, and only departments with an average salary of more than 60,000 are retained.

A real-world business scenario

You are looking for product categories with a total revenue of more than ₹1 lakh.

SELECT category, SUM(revenue) FROM sales GROUP BY category Having SUM(revenue) > 100000;

Because WHERE is aware of aggregated values like SUM(), it cannot be used in this situation. HAVING is precisely used in that situation.

Q5. What is the Difference Between DELETE, DROP, and TRUNCATE?

Though they do it in rather different ways, all three eliminate data:

SQL processes your query in this order:

FROM → WHERE → GROUP BY → HAVING → SELECT

Writing accurate queries requires an understanding of this execution order.

For example, DELETE (delete particular rows):

DELETE FROM employees
WHERE department = ‘HR’;

all HR staff members are eliminated. All other employees are still included in the table.

Example — TRUNCATE (clear the whole table fast):
TRUNCATE TABLE temp_sales;
All rows are gone instantly. The table structure remains. Used for clearing temporary or staging tables.

For example, DROP (remove the table entirely):

DROP TABLE old_backup;

Everything about the table is gone including the data and structure. Use caution when using this.

Interview tip: DELETE can be reversed in a transaction if the interviewer asks, “Which one is reversible?” Typically, TRUNCATE and DROP cannot.

Q6. What is a PRIMARY KEY?

A primary key is a column (or combination of columns) that uniquely identifies every row in a table. It cannot have duplicate values and cannot be NULL.
Every table should have a primary key. Without it, you cannot reliably identify individual records.

For example, the customer table

Priya102Aman, customer_idname101

The primary key in this case is customer_id. There will never be two customers with the same ID.

Why it’s important for business:

Databases use primary keys to connect tables. The customer_id, the primary key that links an order to the right customer, is stored in the orders database at the time of placing orders.

In the absence of primary keys, data may become inconsistent, duplicate rows may occur, and table relationships may break.

Q7. What is a FOREIGN KEY?

A column in one table that points to the primary key of another table is called a foreign key. Relational databases establish relationships between tables in this way.

Orders Referencing Customers, for example:

There is a customer_id column in the orders table. The customer_id primary key in the customers database is referenced by that customer_id. This means that each order must be placed by an actual customer.

SQL query that combines the two tables:

SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id

It enforces business logic:

Multiple orders can be placed by a single consumer. This connection is preserved by the foreign key, which also stops orphan records—orders that don’t actually belong to a customer.

Q8. What is the Difference Between CHAR and VARCHAR?

CHAR stores fixed-length text. VARCHAR stores variable-length text.

Data Type	Storage	Best For
CHAR(n)	Always uses n characters	Fixed values like gender codes
VARCHAR(n)	Uses only what is needed	Names, emails, addresses

Example — CHAR:
gender CHAR(1)
This always stores exactly 1 character — either ‘M’ or ‘F’. CHAR is efficient when you know the exact length.

Example — VARCHAR:
name VARCHAR(100)
A name like “Priya” uses 5 characters of storage. A name like “Rajasekharaiah” uses 14. VARCHAR adapts to the actual length.

Interview tip: Using CHAR for variable-length data wastes storage. Using VARCHAR for truly fixed-length data (like country codes) has no real benefit. Choose based on the nature of the data.

Q9. What is NULL?

NULL stands for unknown or missing data. It differs from both zero and an empty string. NULL indicates that the value was either not provided or does not exist.

Example:

customer	phone
Rahul	NULL

Rahul’s phone number is unknown; it’s neither blank nor zero.

Why this is important for business:

KPIs, counts, and averages are all slightly affected by the NULL data. Your SMS marketing reach is incorrect before you even begin if 20% of your consumer records have NULL phone numbers.

How to properly handle NULL:
WHERE salary = NULL

— Correct
WHERE salary IS NULL

Questions 13 and 14 following go into further detail on this.

Q10. What is the Difference Between COUNT(*) and COUNT(column)?

All rows, including those with NULL values, are counted using COUNT (*). Only rows where that particular field is not NULL are counted by COUNT(column).

For example, COUNT(*):

SELECT COUNT(*)
FROM employees;

returns the entire number of workers, including those whose data is missing.

For example, COUNT(email):
SELECT COUNT(email)
FROM employees;

Only employees with a recorded email address are returned.

Actual business use of SQL interview questions concepts:

SELECT COUNT(email)
FROM customers;

This is done by a marketing team prior to an email campaign. The marketing will only reach 7,500 people if there are 10,000 customers but only 7,500 have emails. Here, COUNT(*) would present an inaccurate image.

Q11. What is the LIKE Operator?

Finding rows where a text column fits a particular pattern is done using LIKE. Two wildcards are used in its operation:

Meaning of the Symbol: % Any character, including zero; _Exactly one character

For example, names that begin with A:

FROM customers
WHERE name LIKE ‘A%’;

Finds: Anybody whose name begins with A, including Aman, Akash, Anita, and Abhishek.

For example, only Gmail users:

SELECT *
FROM customers
WHERE email LIKE ‘%gmail.com’;

This is used by a company to target people on a certain email platform through customer segmentation.

For example, a product search

SELECT *
FROM products
WHERE product_name LIKE ‘%phone%’;

finds any product—smartphone, headphones, phone cases, etc.—that has the word “phone” in its name.

Q12. What is the Difference Between IN and EXISTS?

A column value is compared to a list or subquery result using IN. EXISTS determines whether a subquery returns any rows at all; it doesn’t give a damn about the values; it only looks for matching rows.

For large datasets, EXISTS is typically more effective.

For example, IN

SELECT *
FROM employees
WHERE department_id IN (1, 2, 3);

Easy to read and understand for short lists.

For example, EXISTS

FROM customers c
WHERE EXISTS (
SELECT 1
FROM orders o
WHERE c.customer_id = o.customer_id
);

identifies customers who have made at least one purchase. EXISTS is faster on large tables since it stops searching as soon as it finds the first match.

INEXISTS: The best large datasets and little lists. Style: More straightforward and effective

Q13. How Do You Find NULL Values?

Make use of IS NULL. Since NULL denotes the lack of an value rather than a value that can be compared with equals, you cannot use ‘= NULL’.

For example, identify employees whose salaries are missing:

FROM employees
WHERE salary IS NULL;

For example, identify customers who do not have emails:

FROM customers
WHERE email IS NULL;

Business use: before launching a campaign, determine which client profiles are lacking. These records must be removed or cleansed.

Q14. Why is = NULL Wrong?

Since NULL stands for unknown. Furthermore, SQL is unable to verify that “unknown equals unknown.” Any comparison with NULL yields NULL itself, neither TRUE nor FALSE.

Incorrect:
WHERE salary = NULL—Never returns anything
Correct:
WHERE salary IS NULL: Expected results

Impact on business of making a mistake:

All records with missing values are silently ignored when “= NULL” is used in a report. The totals on your dashboard are incorrect. Decisions are made by stakeholders with insufficient information. Interviewers explicitly check for this common beginner error.

Q15. What is the BETWEEN Operator?

BETWEEN is inclusive, which means that both the start and finish values are included in the results; it filters rows inside a given range.

For example, the salary range
Choose employees whose salaries fall between $50,000 and $100,000.
returns workers who make between ₹50,000 and ₹100,000, covering both destinations.

For example, the monthly reporting date range is:
Choose from the orders
Analysts pull data for monthly reports using the formula WHERE order_date BETWEEN ‘2025-01-01’ AND ‘2025-01-31’.

For example, a product price filter
FROM products, SELECT *
when the cost falls between $1,000 to $5,000.

helpful for analyzing the success of mid-range products or assisting the pricing team in determining the most popular sector.

Frequently Asked SQL Interview Questions

Last Words from My Side

These 15 questions are genuinely the most commonly asked for SQL basics in data analyst interviews. I am aware of this because I have been reading job descriptions, doing mock interviews, and keeping notice of common patterns.Practicing SQL interview questions regularly is the best way to improve your confidence for analyst interviews.
To be honest, I would advise against simply memorizing the syntax. Recognize the rationale behind each idea. Interviewers can tell the difference between someone who can memorize a response and someone who truly comprehends what is going on in the database.

Learning SQL Interview Questions for Data Analysts is important for freshers entering analytics.By getting ready early, you’re doing a great job. Continue.

What's Next?

This is Part 1 of my 100 SQL + Business Data Analytics Interview Questions series on Bloomindata.
Coming up next:

Read Next: SQL Joins — INNER, LEFT, RIGHT, CROSS, SELF
Part 3: Window Functions — ROW_NUMBER, RANK, LAG, LEAD (Q26–Q30)
Part 4: Subqueries and CTEs (Q23–Q25, Q32–Q33)
Part 5: Business Metrics — Churn, LTV, DAU, Retention (Q44–Q50)

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