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Cracking the Data Engineering Interview

You're reading from   Cracking the Data Engineering Interview Land your dream job with the help of resume-building tips, over 100 mock questions, and a unique portfolio

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837630776
Length 196 pages
Edition 1st Edition
Languages
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Authors (2):
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Kedeisha Bryan Kedeisha Bryan
Author Profile Icon Kedeisha Bryan
Kedeisha Bryan
Taamir Ransome Taamir Ransome
Author Profile Icon Taamir Ransome
Taamir Ransome
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1: Landing Your First Data Engineering Job
2. Chapter 1: The Roles and Responsibilities of a Data Engineer FREE CHAPTER 3. Chapter 2: Must-Have Data Engineering Portfolio Projects 4. Chapter 3: Building Your Data Engineering Brand on LinkedIn 5. Chapter 4: Preparing for Behavioral Interviews 6. Part 2: Essentials for Data Engineers Part I
7. Chapter 5: Essential Python for Data Engineers 8. Chapter 6: Unit Testing 9. Chapter 7: Database Fundamentals 10. Chapter 8: Essential SQL for Data Engineers 11. Part 3: Essentials for Data Engineers Part II
12. Chapter 9: Database Design and Optimization 13. Chapter 10: Data Processing and ETL 14. Chapter 11: Data Pipeline Design for Data Engineers 15. Chapter 12: Data Warehouses and Data Lakes 16. Part 4: Essentials for Data Engineers Part III
17. Chapter 13: Essential Tools You Should Know 18. Chapter 14: Continuous Integration/Continuous Development (CI/CD) for Data Engineers 19. Chapter 15: Data Security and Privacy 20. Chapter 16: Additional Interview Questions
21. Index 22. Other Books You May Enjoy

What this book covers

Chapter 1, The Roles and Responsibilities of a Data Engineer, explores the complex array of responsibilities that comprise the core of a data engineer’s role. This chapter unifies the daily responsibilities, long-term projects, and collaborative obligations associated with the title, thereby offering a comprehensive perspective of the profession.

Chapter 2, Must-Have Data Engineering Portfolio Projects, this chapter helps you dive deep into a selection of key projects that can showcase your prowess in data engineering, offering potential employers tangible proof of your capabilities.

Chapter 3, Building Your Data Engineering Brand on LinkedIn, this chapter shows you how to make the most of LinkedIn to show off your accomplishments, skills, and goals in the field of data engineering.

Chapter 4, Preparing for Behavioral Interviews, Along with technical skills, the most important thing is that you can fit in with your team and the company’s culture. There are tips in this chapter on how to do well in behavioral interviews so that you can talk about your strengths and values clearly.

Chapter 5, Essential Python for Data Engineers, Python is still an important tool for data engineers. This chapter will help you learn about the Python ideas, libraries, and patterns that every data engineer needs to know.

Chapter 6, Unit Testing, In data engineering, quality assurance is a must. This chapter will teach you the basics of unit testing to make sure that your data processing scripts and pipelines are reliable and strong.

Chapter 7, Database Fundamentals, At the heart of data engineering lies the database. In this chapter you will acquaint yourself with the foundational concepts, types, and operations of databases, establishing a solid base for advanced topics.

Chapter 8, Essential SQL for Data Engineers, SQL is the standard language for working with data. This chapter will help you learn the ins and outs of SQL queries, optimizations, and best practices so that getting and changing data is easy.

Chapter 9, Database Design and Optimization, It’s both an art and a science to make databases work well. This chapter will teach you about advanced design principles and optimization methods to make sure your databases are quick, scalable, and reliable.

Chapter 10, Data Processing and ETL, Turn raw data into insights that can be used. In this chapter we will learn about the tools, techniques, and best practices of data processing in this chapter, which is about the Extract, Transform, Load (ETL) process.

Chapter 11, Data Pipeline Design for Data Engineers, A data-driven organization needs to be able to easily move data from one place to another. In this chapter you will learn about the architecture, design, and upkeep of data pipelines to make sure that data moves quickly and reliably.

Chapter 12, Data Warehouses and Data Lakes, Explore the huge world of ways to store data. This chapter teaches you the differences between data warehouses and data lakes, as well as their uses and architectures, to be ready for the challenges of modern data.

Chapter 13, Essential Tools You Should Know About, It’s important to have the right tool. In this chapter you will learn how to use the most important tools in the data engineering ecosystem, from importing data to managing it and keeping an eye on it.

Chapter 14, Continuous Integration/Continuous Development for Data Engineers, Being flexible is important in a world where data is always changing. In data engineering and in this chapter, you will learn how to use CI/CD to make sure that data pipelines and processes are always up-to-date and running at their best.

Chapter 15, Data Security and Privacy, It’s important to be responsible when you have a lot of data. This chapter will teach you about the important issues of data security and privacy, and get to know the best ways to protect your data assets and the tools you can use to do so.

Chapter 16, Additional Interview Questions, Getting ready is half the battle won. This chapter comprises of carefully chosen set of interview questions that cover a wide range of topics, from technical to situational. This way, you’ll be ready for any surprise that comes your way.

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