Essential SQL for Data Engineers

This article is an excerpt from the book, Cracking the Data Engineering Interview, by Kedeisha Bryan, Taamir Ransome. The book is a practical guide that’ll help you prepare to successfully break into the data engineering role. The chapters cover technical concepts as well as tips for resume, portfolio, and brand building to catch the employer's attention, while also focusing on case studies and real-world interview questions.

Introduction 

In the world of data engineering, SQL is the unsung hero that empowers us to store, manipulate, transform, and migrate data easily. It is the language that enables data engineers to communicate with databases, extract valuable insights, and shape data to meet their needs. Regardless of the nature of the organization or the data infrastructure in use, a data engineer will invariably need to use SQL for creating, querying, updating, and managing databases. As such, proficiency in SQL can often the difference between a good data engineer and a great one. 

Whether you are new to SQL or looking to brush up your skills, this chapter will serve as a comprehensive guide. By the end of this chapter, you will have a solid understanding of SQL as a data engineer and be prepared to showcase your knowledge and skills in an interview setting. 

In this article, we will cover the following topics: 

• Must-know foundational SQL concepts 
• Must-know advanced SQL concepts 
• Technical interview questions 

Must-know foundational SQL concepts 

In this section, we will delve into the foundational SQL concepts that form the building blocks of data engineering. Mastering these fundamental concepts is crucial for acing SQL-related interviews and effectively working with databases. 

Let’s explore the critical foundational SQL concepts every data engineer should be comfortable with, as follows: 

• SQL syntax: SQL syntax is the set of rules governing how SQL statements should be written. As a data engineer, understanding SQL syntax is fundamental because you’ll be writing and reviewing SQL queries regularly. These queries enable you to extract, manipulate, and analyze data stored in relational databases. 
• SQL order of operations: The order of operations dictates the sequence in which each of the following operators is executed in a query: 

FROM and JOIN 

WHERE 

GROUP BY 

HAVING 

SELECT 

DISTINCT 

ORDER BY 

LIMIT/OFFSET 

• Data types: SQL supports a variety of data types, such as INT, VARCHAR, DATE, and so on. Understanding these types is crucial because they determine the kind of data that can be stored in a column, impacting storage considerations, query performance, and data integrity. As a data engineer, you might also need to convert data types or handle mismatches. 
• SQL operators: SQL operators are used to perform operations on data. They include arithmetic operators (+, -, *, /), comparison operators (>, <, =, and so on), and logical operators (AND, OR, and NOT). Knowing these operators helps you construct complex queries to solve intricate data-related problems. 
• Data Manipulation Language (DML), Data Definition Language (DDL), and Data Control  

Language (DCL) commands: DML commands such as SELECT, INSERT, UPDATE, and DELETE allow you to manipulate data stored in the database. DDL commands such as CREATE, ALTER, and DROP enable you to manage database schemas. DCL commands such as GRANT and REVOKE are used for managing permissions. As a data engineer, you will frequently use these commands to interact with databases. 

• Basic queries: Writing queries to select, filter, sort, and join data is an essential skill for any data engineer. These operations form the basis of data extraction and manipulation. 
• Aggregation functions: Functions such as COUNT, SUM, AVG, MAX, MIN, and GROUP BY are used to perform calculations on multiple rows of data. They are essential for generating reports and deriving statistical insights, which are critical aspects of a data engineer’s role. 

The following section will dive deeper into must-know advanced SQL concepts, exploring advanced techniques to elevate your SQL proficiency. Get ready to level up your SQL game and unlock new possibilities in data engineering! 

Must-know advanced SQL concepts 

This section will explore advanced SQL concepts that will elevate your data engineering skills to the next level. These concepts will empower you to tackle complex data analysis, perform advanced data transformations, and optimize your SQL queries. 

Let’s delve into must-know advanced SQL concepts, as follows: 

• Window functions: These do a calculation on a group of rows that are related to the current row. They are needed for more complex analyses, such as figuring out running totals or moving averages, which are common tasks in data engineering. 
• Subqueries: Queries nested within other queries. They provide a powerful way to perform complex data extraction, transformation, and analysis, often making your code more efficient and readable. 
• Common Table Expressions (CTEs): CTEs can simplify complex queries and make your code more maintainable. They are also essential for recursive queries, which are sometimes necessary for problems involving hierarchical data. 
• Stored procedures and triggers: Stored procedures help encapsulate frequently performed tasks, improving efficiency and maintainability. Triggers can automate certain operations, improving data integrity. Both are important tools in a data engineer’s toolkit. 
• Indexes and optimization: Indexes speed up query performance by enabling the database to locate data more quickly. Understanding how and when to use indexes is key for a data engineer, as it affects the efficiency and speed of data retrieval. 
• Views: Views simplify access to data by encapsulating complex queries. They can also enhance security by restricting access to certain columns. As a data engineer, you’ll create and manage views to facilitate data access and manipulation. 

By mastering these advanced SQL concepts, you will have the tools and knowledge to handle complex data scenarios, optimize your SQL queries, and derive meaningful insights from your datasets. The following section will prepare you for technical interview questions on SQL. We will equip you with example answers and strategies to excel in SQL-related interview discussions. Let’s further enhance your SQL expertise and be well prepared for the next phase of your data engineering journey. 

Technical interview questions 

This section will address technical interview questions specifically focused on SQL for data engineers. These questions will help you demonstrate your SQL proficiency and problem-solving abilities. Let’s explore a combination of primary and advanced SQL interview questions and the best methods to approach and answer them, as follows: 

• Question 1: What is the difference between the WHERE and HAVING clauses? 

Answer: The WHERE clause filters data based on conditions applied to individual rows, while the HAVING clause filters data based on grouped results. Use WHERE for filtering before aggregating data and HAVING for filtering after aggregating data. 

• Question 2: How do you eliminate duplicate records from a result set? 

Answer: Use the DISTINCT keyword in the SELECT statement to eliminate duplicate records and retrieve unique values from a column or combination of columns. 

• Question 3: What are primary keys and foreign keys in SQL? 

Answer: A primary key uniquely identifies each record in a table and ensures data integrity. A foreign key establishes a link between two tables, referencing the primary key of another table to enforce referential integrity and maintain relationships. 

• Question 4: How can you sort data in SQL? 

Answer: Use the ORDER BY clause in a SELECT statement to sort data based on one or more columns. The ASC (ascending) keyword sorts data in ascending order, while the DESC (descending) keyword sorts it in descending order. 

• Question 5: Explain the difference between UNION and UNION ALL in SQL. 

Answer: UNION combines and removes duplicate records from the result set, while UNION ALL combines all records without eliminating duplicates. UNION ALL is faster than UNION because it does not involve the duplicate elimination process. 

• Question 6: Can you explain what a self join is in SQL? 

Answer: A self join is a regular join where a table is joined to itself. This is often useful when the data is related within the same table. To perform a self join, we have to use table aliases to help SQL distinguish the left from the right table. 

• Question 7: How do you optimize a slow-performing SQL query? 

Answer: Analyze the query execution plan, identify bottlenecks, and consider strategies such as creating appropriate indexes, rewriting the query, or using query optimization techniques such as JOIN order optimization or subquery optimization.  

• Question 8: What are CTEs, and how do you use them? 

Answer: CTEs are temporarily named result sets that can be referenced within a query. They enhance query readability, simplify complex queries, and enable recursive queries. Use the WITH keyword to define CTEs in SQL. 

• Question 9: Explain the ACID properties in the context of SQL databases. 

Answer: ACID is an acronym that stands for Atomicity, Consistency, Isolation, and Durability. These are basic properties that make sure database operations are reliable and transactional. Atomicity makes sure that a transaction is handled as a single unit, whether it is fully done or not. Consistency makes sure that a transaction moves the database from one valid state to another. Isolation makes sure that transactions that are happening at the same time don’t mess with each other. Durability makes sure that once a transaction is committed, its changes are permanent and can survive system failures. 

• Question 10: How can you handle NULL values in SQL? 

Answer: Use the IS NULL or IS NOT NULL operator to check for NULL values. Additionally, you can use the COALESCE function to replace NULL values with alternative non-null values. 

• Question 11: What is the purpose of stored procedures and functions in SQL? 

Answer: Stored procedures and functions are reusable pieces of SQL code encapsulating a set of SQL statements. They promote code modularity, improve performance, enhance security, and simplify database maintenance. 

• Question 12: Explain the difference between a clustered and a non-clustered index. 

Answer: The physical order of the data in a table is set by a clustered index. This means that a table can only have one clustered index. The data rows of a table are stored in the leaf nodes of a clustered index. A non-clustered index, on the other hand, doesn’t change the order of the data in the table. After sorting the pointers, it keeps a separate object in a table that points back to the original table rows. There can be more than one non-clustered index for a table. 

Prepare for these interview questions by understanding the underlying concepts, practicing SQL queries, and being able to explain your answers. 

Conclusion

This article explored the foundational and advanced principles of SQL that empower data engineers to store, manipulate, transform, and migrate data confidently. Understanding these concepts has unlocked the door to seamless data operations, optimized query performance, and insightful data analysis. 

SQL is the language that bridges the gap between raw data and valuable insights. With a solid grasp of SQL, you possess the skills to navigate databases, write powerful queries, and design efficient data models. Whether preparing for interviews or tackling real-world data engineering challenges, the knowledge you have gained in this chapter will propel you toward success. 

Remember to continue exploring and honing your SQL skills. Stay updated with emerging SQL technologies, best practices, and optimization techniques to stay at the forefront of the ever-evolving data engineering landscape. Embrace the power of SQL as a critical tool in your data engineering arsenal, and let it empower you to unlock the full potential of your data. 

Author Bio

Kedeisha Bryan is a data professional with experience in data analytics, science, and engineering. She has prior experience combining both Six Sigma and analytics to provide data solutions that have impacted policy changes and leadership decisions. She is fluent in tools such as SQL, Python, and Tableau.
She is the founder and leader at the Data in Motion Academy, providing personalized skill development, resources, and training at scale to aspiring data professionals across the globe. Her other works include another Packt book in the works and an SQL course for LinkedIn Learning.

Taamir Ransome is a Data Scientist and Software Engineer. He has experience in building machine learning and artificial intelligence solutions for the US Army. He is also the founder of the Vet Dev Institute, where he currently provides cloud-based data solutions for clients. He holds a master's degree in Analytics from Western Governors University.