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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
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Taamir Ransome
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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

Data Warehouses and Data Lakes

In today’s data-driven society, organizations are constantly seeking methods to manage, process, and extract insights from vast quantities of data. Traditional databases and processing systems often fall short in terms of scalability, flexibility, and performance as data volume, velocity, and variety increase. Various systems and architectures are utilized by businesses for different purposes, ranging from transactional data in databases to big data in cloud storage. You’ll probably come across two main categories of data storage architectures: data lakes and warehouses.

We will delve into these two fundamental components in this chapter. You will discover the underlying ideas that distinguish them, the subtleties of their architecture, and the data flows that occur both inside and between these systems.

In this chapter, we will cover the following topics:

  • Exploring data warehouse essentials for data engineers
  • Examining...
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