You're reading from Azure Data Engineer Associate Certification Guide A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam

Product type Paperback

Published in Feb 2022

Publisher Packt

ISBN-13 9781801816069

Length 574 pages

Edition 1st Edition

Tools

Azure

Concepts

Big Data

Author (1):

Newton Alex

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1: Azure Basics

2. Chapter 1: Introducing Azure Basics FREE CHAPTER

3. Part 2: Data Storage

4. Chapter 2: Designing a Data Storage Structure

5. Chapter 3: Designing a Partition Strategy

6. Chapter 4: Designing the Serving Layer

7. Chapter 5: Implementing Physical Data Storage Structures

8. Chapter 6: Implementing Logical Data Structures

9. Chapter 7: Implementing the Serving Layer

10. Part 3: Design and Develop Data Processing (25-30%)

11. Chapter 8: Ingesting and Transforming Data

12. Chapter 9: Designing and Developing a Batch Processing Solution

13. Chapter 10: Designing and Developing a Stream Processing Solution

14. Chapter 11: Managing Batches and Pipelines

15. Part 4: Design and Implement Data Security (10-15%)

16. Chapter 12: Designing Security for Data Policies and Standards

17. Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)

18. Chapter 13: Monitoring Data Storage and Data Processing

19. Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing

20. Part 6: Practice Exercises

21. Chapter 15: Sample Questions with Solutions

22. Other Books You May Enjoy

Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing

Welcome to the final chapter in the Monitoring and Optimizing Data Storage and Data Processing section of the syllabus. The only chapter left after this is the revision and sample questions for the certification. Congratulations on reaching this far; you are now just a hop away from acquiring your certification.

In this chapter, we will be focusing on the optimization and troubleshooting techniques for data storage and data processing technologies. We will start with the topics for optimizing Spark and Synapse SQL queries using techniques such as compacting small files, handling UDFs, data skews, shuffles, indexing, cache management, and more. We will also look into techniques for troubleshooting Spark and Synapse pipelines and general guidelines for optimizing any analytical pipeline. Once you complete this chapter, you will have the knowledge to debug performance issues or troubleshoot failures in pipelines...