Top 24 SQL Interview Questions for Data Analysts

So you’re getting interview ready? Perfect, you’re in the right place.

We’ve compiled the “core 24” interview questions you need to know before your big interview.

Read on.

SQL interview questions could include:

1. What is the purpose of the SQL GROUP BY clause?

2. What is the SQL ORDER BY clause, and how is it used?

3. Write a query to fetch the average salary for employees in the “Sales” department.

4. Write a query to retrieve employees from the “Employee” table with a salary greater than $50,000.

5. Explain the difference between an INNER JOIN and a LEFT JOIN with examples.

In this article, we will discuss important SQL interview questions that span across various difficulty levels.

From easy to intermediate and even hard questions, we aim to cover the most relevant and frequently asked questions that test your understanding of SQL concepts.

By familiarizing yourself with these questions, you can be better prepared to tackle SQL interview challenges and increase your chances of landing your next data analyst role.

Let’s dive in!

SQL Basics You Should Know

SQL, or Structured Query Language, is a programming language specifically designed for managing and querying relational databases. As a data analyst, mastering SQL is crucial for working with data stored in relational database management systems (RDBMS).

When you’re using SQL, you’ll often come across various SQL terms. These terms relate to specific features or components of the language.

Here’s an outline of some fundamental SQL concepts you should know:

• Tables: They store data as rows and columns, similar to a spreadsheet. Each row represents a record, and each column represents a data field.

• Primary Key: A primary key is a column (or set of columns) used to uniquely identify each row in a table.

• Foreign Key: A foreign key is a column that refers to the primary key of another table, enabling you to establish relationships between tables.

• Joins: Joins allow you to retrieve related data from multiple tables simultaneously. The main types of join are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.

• SELECT Statement: It is one of the fundamental SQL operations and is used to retrieve data from one or more tables.

• Relational Databases: This consists of tables with rows and columns that store related data. These tables are connected through relationships, defined by primary and foreign keys.

When preparing for a data analyst interview, ensure that you have a strong grasp of these concepts and can explain their importance clearly and concisely.

Now let’s get to some interview questions.

SQL Interview Topics For Data Analysts

Prospective employers often use SQL questions in interviews for data analyst positions to assess candidates’ ability to work with data effectively.

These questions span various SQL topics, from the foundational to the more advanced.

These topics include:

1. Basic SQL Commands

2. Advanced SQL Concepts

3. SQL Queries

4. Large Datasets Handling

Now, let’s check out basic SQL commands.

Topic 1: Basic SQL Commands

SQL (Structured Query Language) is the cornerstone of managing and manipulating data within relational databases. Furthermore, familiarity with basic SQL commands is crucial for any data analyst.

Here are some fundamental commands frequently used in SQL interviews:

1. Data Extraction

In SQL, data extraction is essential for obtaining information from databases. Some vital commands for data extraction include:

• SELECT: To choose specific columns from a table, use this command. You can use an asterisk (*) to select all columns.

• FROM: Specify the table or tables you’re querying data from.

• WHERE: Filter the rows based on a specified condition.

• ORDER BY: Sort the result set based on a specific column or columns in ascending (ASC) or descending (DESC) order.

When extracting data from multiple tables, you can use different types of joins:

• Inner Join: Returns rows from both tables when there is a match between the columns specified in the join condition.

• Left Join (or LEFT OUTER JOIN): Returns all rows from the left table and the matched rows from the right table. If there’s no match, NULL values fill in the right side.

Question sample 1: Retrieve the names and purchase amounts of all customers who made a purchase in the year 2023 from two tables, “Customers” and “Purchases.” Sort the results in descending order of purchase amount.

Question sample 2: Obtain a list of employees and the departments they work in by combining data from the “Employees” and “Departments” tables. Include only those employees who belong to a department.

2. Data Manipulation

The data manipulation commands help you modify existing data, insert new data, and delete unnecessary data from your database. A few common Data Manipulation Language (DML) commands include:

• INSERT: Add new rows to a table with specific values for each column.

• UPDATE: Modify existing rows in a table by specifying new values for specific columns.

• DELETE: Remove particular rows from a table based on a specified condition.

Question sample 3: Imagine you need to change the shipping address for a specific customer in the “Customers” table. Write an SQL statement to update the shipping address for the customer with the ID of ‘123’ to ‘123 Main Street.’

Question sample 4: Suppose there are redundant records in the “Orders” table, and you need to remove all orders with a status of ‘Cancelled.’ Write an SQL statement to delete all such records from the table.

3. Data Control

Data control commands help manage and control access to database objects. While these DCL (Data Control Language) commands are not as frequently used during SQL interviews for data analysts, they contribute to a comprehensive understanding of SQL. Here are some examples:

• GRANT: Assign specific privileges to users or user groups, such as SELECT, INSERT, UPDATE, and DELETE, on database objects like tables, views, and columns.

• REVOKE: Remove or restrict previously assigned privileges from users or user groups.

By familiarizing yourself with these basic SQL commands, you can demonstrate your ability to extract, manipulate, and control data during a data analyst interview.

Question sample 5: Using SQL, demonstrate how to grant SELECT permission on the ‘Sales’ table to a new user ‘AnalystUser.’ Ensure that ‘AnalystUser’ has read-only access to the data.

Question sample 6: Explain the process of revoking INSERT, UPDATE, and DELETE permissions from the ‘HR’ user for the ‘EmployeeData’ table. Provide the SQL commands to achieve this while keeping the SELECT permission intact.

4. Data Definition

Data definition commands, part of the Data Definition Language (DDL), are used to define, modify, and manage the structure of a database. These commands help create tables, constraints, indexes, and other database objects.

While DDL commands are not tested as frequently in SQL interviews for data analysts as the more frequently used DML and DQL commands, a solid understanding of them is essential for comprehensive SQL knowledge.

Here are some key DDL commands and their applications:

• CREATE: This command creates new database objects, such as tables, views, indexes, or constraints. It specifies the structure, attributes, and relationships of the object being created.

• ALTER: ALTER commands modify existing database objects. For example, you can add, modify, or drop columns in a table, change data types, or rename objects.

• DROP: DROP commands are employed to delete database objects, including tables, views, indexes, and constraints. Be cautious when using DROP, as it permanently removes the specified object and its data.

• TRUNCATE: TRUNCATE is used to remove all rows from a table quickly, but it retains the table’s structure for further use.

By understanding DDL commands, you can control the structure and organization of a database, ensuring that it accurately represents the data it stores.

Question Sample 7: Explain how to create a new table named “Customers” with columns for “CustomerID,” “Name,” and “Email” using SQL’s CREATE command.

Question Sample 8: Describe the process of adding a new column called “PhoneNumber” to an existing table named “Contacts.” Write the SQL ALTER command to achieve this without affecting the existing data.

Once you are confident you can answer any question on basic SQL concepts, it’s time to prepare for some advanced SQL concepts.

Topic 2: Advanced SQL Concepts

In this section, we discuss some advanced SQL concepts you may encounter in interviews.

Understanding these concepts will strengthen your SQL knowledge and confidence during interviews.

Now, let’s explore subqueries, aggregation, and indexing.

1. Subqueries

A subquery is a query embedded within another query, and is also known as an inner or nested query. They are commonly used to filter, sort, or aggregate results based on a separate set of conditions. You can use subqueries in various clauses, such as SELECT, FROM, WHERE, and HAVING. Here is an example of a subquery:

SELECT FirstName, LastName
FROM Employees
WHERE EmployeeID IN (SELECT EmployeeID FROM Sales WHERE Revenue > 10000);

In this example, we retrieve the names of employees with sales revenue greater than 10,000. The subquery retrieves the relevant EmployeeIDs before filtering the main query.

Question sample 9: You have two tables, “Orders” and “Customers.” Write an SQL query that retrieves customers’ names who have placed orders in the “Electronics” category. Use a subquery to filter the results.

Question sample 10: Consider two tables, “Employees” and “Salaries.” Write an SQL query to find the average salary of employees in the “Sales” department. Utilize a subquery to calculate this average based on the department’s data.

2. Aggregation

Data aggregation in SQL refers to the process of summarizing and grouping query results. The main aggregate functions you need to know are COUNT, SUM, AVG, MIN, and MAX. Aggregation commonly involves the GROUP BY clause, which allows you to group rows by specific columns. Here’s an example:

SELECT Department, COUNT(*) AS NumEmployees, AVG(Salary) AS AvgSalary
FROM Employees
GROUP BY Department;

In this query, we group employees by their department and calculate both the number of employees and the average salary for each group.

Question sample 11: Find the average order value for customers with at least three orders. Aggregate the data from the “Orders” and “Customers” tables, and filter the results accordingly.

Question sample 12: Calculate the total sales amount for each product category from the “Sales” table and present the results with the corresponding category names from the “Categories” table.

3. Indexing

Indexes are database structures that can speed up data retrieval by providing a more efficient way to access table rows. They help to organize data based on one or more columns, making queries run faster. There are two main types of indexes:

• Clustered index: An index that determines the physical order of rows in a table. There can only be one clustered index for a table.

• Non-clustered index: An index that does not affect the physical order of rows, instead providing a separate structure that acts as a reference to the table data. You can have multiple non-clustered indexes for a table.

When designing a database schema, you may come across other key concepts, such as:

• Primary key: A unique identifier for each row in a table. It is often a combination of one or more columns and is used to enforce data integrity.

• Foreign key: A column or set of columns in a table that refers to the primary key of another table, establishing a relationship between them.

• Unique key: A constraint that ensures all values in a column are unique, preventing duplicate data.

Remember to consider the use of indexes and keys to optimize your SQL queries and efficiently manage relationships between tables.

Question sample 13: Describe the role of a foreign key in a relational database. Provide an example of two tables with a foreign key relationship and explain how it helps maintain data consistency between the tables.

Question sample 14: Explain the concept of a primary key in a database table. Provide an example of a table and its primary key, highlighting the significance of this key in ensuring data integrity.

Next let’s look at some of the SQL query interview questions you might encounter in your interview.

Topic 3: SQL Queries

When preparing for a data analyst interview, you’ll want to be comfortable with different types of SQL queries. This section will focus on two main categories: single-table queries and multi-table queries.

1. Single-table Queries

Single-table queries involve selecting, filtering, or sorting data from a single table within a database. Here are some key concepts you should be familiar with:

• SELECT statement: This is used to retrieve data from one or more columns of a table. For example:

SELECT column1, column2 FROM table_name;

• WHERE clause: This helps you filter the data based on specific conditions. For example:

SELECT * FROM table_name WHERE column1 = 'value';

• GROUP BY clause: This is used to group rows with the same values in specified columns. Typically used with aggregate functions such as COUNT, SUM, or AVG. For example:

SELECT column1, COUNT(*) FROM table_name GROUP BY column1;

• ORDER BY clause: This is used to sort the results of your query in ascending or descending order based on specific columns. For example:

SELECT * FROM table_name ORDER BY column1 DESC;

Question sample 15: Retrieve the names and purchase amounts of all customers who purchased in the year 2022 from the “Sales” table. Sort the results in ascending order based on the purchase amount.

Question sample 16: Find the total sales amount for each product category from the “Sales” table and present the results with the corresponding category names. Include only categories with a total sales amount exceeding $10,000.

2. Multi-table Queries

In addition to single-table queries, you should also be familiar with multi-table queries, which include operations like JOINs or subqueries. These complex queries help you combine data from multiple tables to answer more complex query-writing questions. Here are some common multi-table query concepts:

• JOIN operations: This combines rows from two or more tables based on a related column. The types of JOINs you should know include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN. For example

SELECT * FROM table1 INNER JOIN table2 ON table1.column1 = table2.column2;

• Subqueries: A subquery is a query embedded within another query (usually within the WHERE clause or HAVING clause). It allows you to retrieve intermediate results from one table that can be used in the main query. For example:

SELECT * FROM table1 WHERE column1 IN (SELECT column1 FROM table2 WHERE column2 = 'value');

Remember to practice writing SQL queries using different combinations of these concepts to effectively prepare for your interview.

Question sample 17: Retrieve a list of all customers who made purchases in the last quarter, along with the names of the products they purchased. Combine data from the “Customers,” “Purchases,” and “Products” tables using appropriate JOIN operations.

Question sample 18: Find the customers who purchased in the “Electronics” category in the “Sales” table. Use a subquery to extract customer information from the “Customers” table based on their purchase history.

In addition to SQL query knowledge, knowing how to handle large datasets is an essential skill for a data analyst. You are bound to be asked about this topic in an interview. Let’s look at some of the questions you may encounter.

Topic 4: Large Datasets Handling

SQL becomes an essential tool for data analysts when working with large datasets. It allows you to efficiently filter, sort, and aggregate data, making your analysis process smoother and more accurate.

In this section, we will focus on some data analyst interview questions related to large datasets and SQL.

1. SQL Queries

Designing SQL queries for large datasets usually requires a deep understanding of database structures and optimization techniques. Your interviewer might ask you to write a query to handle millions of rows or to optimize an existing query for better performance.

Question sample 19: How do you create indexes on large tables to improve query performance?

Question sample 20: Explain the concept of partitioning and how it can be used to manage large datasets.

2. SQL Operations

When working with large datasets, it’s essential to understand the performance implications of different SQL operations. You may be asked about:

• The difference between WHERE and HAVING, and when to use each in the context of large datasets.

• The impact of sub-queries on query performance and potential alternatives such as Common Table Expressions (CTEs) or Window Functions.

• There are benefits and drawbacks of various join types, such as INNER JOIN, LEFT JOIN, and FULL OUTER JOIN, especially when handling large datasets.

3. Aggregate & Window Functions

Mastering aggregate functions and window functions is crucial when dealing with large datasets. Some sample questions related to them are:

Question sample 21: Can you write an SQL query to calculate the running total for a specific column in a large dataset?

Question sample 22: How would you compare the performance of window functions and aggregate functions when working with large datasets?

4. Data Sampling

Finally, when handling large datasets, data sampling and approximate analytics techniques could be discussed during the interview. Questions might include:

Question sample 23: How do you use SQL to create a random sample of data from a large dataset?

Question sample 24: What are approximate analytics techniques, and why are they essential for large datasets?

We’ve covered almost all the topics you can expect in your data analyst interview.

But, acing your interview requires more than just technical skills. Let’s talk about some critical interview skills you need to have to stand out from the crowd.

Mastering SQL Interview Skills for Data Analysts

Preparing for SQL interviews as a data analyst requires technical prowess, problem-solving acumen, and effective communication.

These three essential aspects can set you on the path to interview success.

Here’s how to excel in your SQL interviews:

1. Enhance Problem-Solving Skills

When preparing for an SQL interview as a data analyst, employers value your ability to solve real-world data challenges.

To sharpen your problem-solving skills:

• Practice Real-World Scenarios: Work on practical data sets to understand common issues and how to solve them using SQL.

• Practice Key Topics to Cover:

  • Data Analysis & Aggregation: Master functions like COUNT, AVG, SUM, and MAX. Learn to group and filter data with GROUP BY and HAVING.

  • Data Cleaning & Transformation: Use functions for data cleaning and transformation, such as string and date functions and CASE statements.

  • Relational Database Concepts: Understand table design, normalization, and effective data retrieval using JOINs.

  • Optimization Techniques: Optimize queries for better performance with indexes, partitioning, and query hints.

• Build a Portfolio: Showcase your problem-solving skills with case studies and completed projects. This demonstrates your practical experience.

• Learn from Others: Join online SQL and data analysis communities to access solutions advice, and stay updated on industry trends.

2. Interview Preparation Tips

When getting ready for SQL interviews, focusing on your technical skills is crucial. The interview process can include any or all of these three stages: technical screening, a whiteboard test, and a take-home assignment.

Here’s how to prepare effectively:

• Technical Screening: Practice common SQL interview questions and review key SQL concepts like data manipulation, subqueries, and joins. Seek advice from experienced professionals.

• Whiteboard Test: Prepare by writing SQL queries on paper or a whiteboard, focusing on clear problem-solving and explanation.

• Take-Home Assignment: Practice solving SQL problems requiring data analysis and follow instructions meticulously. Demonstrate your SQL expertise and creativity.

3. Effective Communication Skills

As a data analyst, strong communication is vital for sharing your insights clearly. In interviews, your communication skills may be tested.

Here’s how to shine:

• Restate the Question: Begin by rephrasing the question to confirm your understanding.

• Maintain a Neutral Yet Confident Tone: Project professionalism with a calm and confident response tone.

• Seek Clarification When Needed: Don’t hesitate to ask for clarification to demonstrate attention to detail.

• Use Simple Language and Clear Examples: Explain complex concepts in simple terms, breaking them down into easily understandable parts.

• Practice Active Listening: Pay attention to the interviewer’s questions and maintain appropriate eye contact during the interview.

Excelling in SQL interviews requires a well-rounded approach. Enhance your technical skills, hone your problem-solving abilities, and demonstrate effective communication.

By mastering these three key aspects, you’ll be well-prepared to tackle the challenges of data analyst interviews.

Now let’s look at some popular SQL platforms you should be comfortable with.

Popular SQL Platforms

When preparing for a data analyst interview, it’s essential to familiarize yourself with popular SQL platforms. In this section, we will discuss two widely-used SQL platforms: MySQL and SQL Server.

MySQL

MySQL is an open-source relational database management system (DBMS) that’s widely used for web applications and data warehousing. Since it’s open-source, MySQL offers the advantage of being free to use and easily customizable.

SQL Server

SQL Server, developed by Microsoft, is a powerful and widely used relational database management system.

It’s primarily used in enterprise environments and offers various editions to fit the needs of different organization sizes.

As a data analyst, understanding the capabilities of MySQL and SQL Server will help you make informed decisions when working with data and answering interview questions related to SQL platforms.

Now let’s look at some of the biggest data analyst employers and show you how to prepare for each company.

Case Studies

Google

When preparing for an SQL interview at Google, you can expect questions covering different aspects of SQL knowledge.

Also, you may be asked to analyze data and discover interesting patterns or trends. Google often focuses on questions that test candidates’ ability to optimize queries and improve performance.

For example, you might be given a large dataset of user searches and asked to find the most popular search terms in a specific time range.

To succeed in answering these questions, practice writing complex data analysis tasks, aggregating data, and optimizing query performance.

Amazon

Amazon’s interview process for data analysts often includes several SQL case studies, generally related to the e-commerce domain.

These may involve analyzing sales data, optimizing marketing strategies, or understanding customer behavior.

Practice writing efficient and well-structured queries to excel in Amazon’s SQL case study questions.

Additionally, consider scenarios that require calculations, filtering, and grouping data by various criteria.

Microsoft

Microsoft’s data analyst interview process may involve SQL case study questions related to their wide range of products and services.

You could be asked to analyze telemetry data from an app or service or explore the impact of new features on user engagement.

To prepare for Microsoft’s SQL case study questions, practice writing advanced SQL queries using joins, window functions, and other complex operations. Focus on delivering clear and concise insights by manipulating and aggregating data effectively.

Uber

In an Uber SQL interview, you can expect case study questions related to the transportation and ride-sharing industry.

Questions might involve analyzing data on driver performance, predicting demand, or understanding user behavior about pricing and promotions.

To excel in Uber’s SQL case study questions, practice writing queries to extract insights from diverse datasets.

Familiarize yourself with geospatial data, groupings, and aggregations to showcase your ability to work with complex data structures and glean meaningful information.

Final Thoughts

SQL is the backbone of data analysis, and a strong command of SQL is vital for success in a data analyst role.

As this article explores, SQL interview questions for data analysts can cover a wide range of topics, from fundamental syntax to advanced query optimization.

By preparing and practicing these questions, you’ll showcase your technical skills, problem-solving abilities, and effective communication.

Remember, mastering SQL is a journey that continues to evolve as you gain experience and adapt to new challenges in the world of data analysis.

So, keep learning, stay curious, and approach SQL interviews with confidence, knowing that your proficiency in SQL will open doors to exciting opportunities in the data analytics field.

Good luck with your future interviews!

Ready to use AI to assist in SQL? Try polishing up your SQL skills with ChatGPT; check out our latest clip below.

Frequently Asked Questions

What are the different types of SQL joins and how do they function?

SQL joins are used to combine data from two or more tables based on a related column. There are four basic types of joins:

1. Inner Join: Returns rows where there is a match in both tables.

2. Left Join: Returns all rows from the left table and the matched rows from the right table. If no match is found, NULL values are displayed.

3. Right Join: Returns all rows from the right table and the matched rows from the left table. If no match is found, NULL values are displayed.

4. Full Outer Join: Returns all rows when there is a match in either the left or the right table. If no match is found, NULL values are displayed.

Describe the functions of GROUP BY and ORDER BY in SQL.

GROUP BY and ORDER BY clauses serve different purposes in SQL queries:

• GROUP BY: This clause is used to group rows with the same values in specified columns, typically alongside aggregate functions like COUNT(), SUM(), AVG(), etc.

• ORDER BY: This clause is used to sort the result set by one or more columns in ascending (ASC) or descending (DESC) order.

How do you create a stored procedure and what are their uses in data analysis?

A stored procedure is a pre-compiled group of SQL statements that can be executed multiple times with different parameters. To create a stored procedure, you use the CREATE PROCEDURE keyword, followed by the name of the procedure and its parameters.

Stored procedures can be beneficial for data analysis because they:

1. Improve performance by reducing network traffic and pre-compiling SQL statements.

2. Provide a level of security by limiting direct access to underlying data.

3. Encapsulate complex logic to simplify data manipulation and retrieval.

4. Facilitate code reuse and modularity.

What is the difference between UNION and UNION ALL in SQL?

Both UNION and UNION ALL combine the result sets of two or more SELECT statements into a single result set.

The main differences are:

• UNION: Merges the result sets and removes any duplicate rows. It internally sorts the data and performs an extra step to ensure uniqueness.

• UNION ALL: Combines the result sets and keeps all rows, including duplicates. It does not perform any sorting or filtering, which makes it faster than UNION.

How do you use window functions and explain their significance in data analysis?

Window functions are used to perform calculations on a set of rows related to the current row, without collapsing the rows into a single aggregation. You use a window function by specifying the function name, followed by an OVER() clause that defines the window or range of rows.

Window functions are significant in data analysis because they:

1. Allow complex calculations on ordered or partitioned data.

2. Enable calculations based on relative positions within a result set.

3. Provide an efficient way to handle ranking, cumulative sums, moving averages, and other calculations more flexibly than traditional aggregate functions.