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

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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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Data Processing and ETL

Navigating the intricacies of data engineering roles requires an in-depth understanding of data processing and Extract, Transform, and Load (ETL) processes. Not only do these foundational skills form the foundation upon which data pipelines are constructed but they are also integral components of the data engineering interview landscape. Therefore, mastering them is a prerequisite for anyone seeking success in data engineering roles. In this chapter, we will delve into the nitty-gritty details of implementing ETL processes, examine the various paradigms of data processing, and guide you on how to prepare for technical data engineering interview questions. This chapter aims to equip you with the knowledge and skills necessary to ace data engineering interviews by providing real-world scenarios, technical questions, and best practices.

In this chapter, we will cover the following topics: