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

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781837630776

Length 196 pages

Edition 1st Edition

Languages

Python

Tools

Core Data

Concepts

Data Engineering

Authors (2):

Kedeisha Bryan

Taamir Ransome

View More author details

Table of Contents (23) Chapters

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

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Fundamental concepts

Before delving into the complexities of ETL and data processing, it is essential to lay a solid foundation by understanding the underlying concepts and architectures. This section serves as a guide to the fundamental concepts that every data engineer should understand. By the end of this section, you should have a comprehensive understanding of the essential frameworks and terminology for both practical applications and interview success.

The life cycle of an ETL job

The life cycle of an ETL job is a well-orchestrated sequence of steps designed to move data from its source to a destination, usually a data warehouse, while transforming it into a usable format. The process begins with extraction, the phase in which data is extracted from multiple source systems. These systems could be databases, flat files, application programming interfaces, or even web scraping targets. The key is to extract the data in a manner that minimizes the impact on source systems...