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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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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

Pipeline catch-up and recovery

In the world of data engineering, failure is not a question of if but when. Data pipeline failures are inevitable, regardless of whether they are caused by server outages, network problems, or code bugs. The ability to recover from these failures is what differentiates a well-designed pipeline from a fragile one. Understanding the types of failures that can occur and their potential impact on your pipeline is the first step in designing a resilient system.

Through a combination of redundancy, fault tolerance, and quick recovery mechanisms, data pipelines achieve resilience. Redundancy is the presence of backup systems in the event of a system failure. Fault tolerance is the process of designing a pipeline to continue operating, albeit at a reduced capacity, even if some components fail. Quick recovery mechanisms, on the other hand, ensure that the system can resume full operation as quickly as possible following a failure.

When a data pipeline fails...

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