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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Technical interview questions

In this section, we will prepare you for technical interview questions specifically focused on data pipeline design. These questions aim to assess your understanding of the concepts and practical considerations involved in designing efficient and reliable data pipelines:

Question 1: What is the difference between ETL and ELT?
Answer: ETL involves the extraction of data from source systems, its transformation into a usable format, and its loading into a target database or data warehouse. In contrast, ELT involves extracting data and loading it into the target system before transformation. ELT is typically more effective when the target system is robust enough to handle transformations quickly, such as modern cloud-based data warehouses such as Snowflake or BigQuery.
Question 2: How would you ensure data quality in your pipeline?
Answer: Data quality can be maintained by incorporating validation checks at various pipeline stages. For instance,...